{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\mjoshi2.DSS-OKIU3QJMAS9\Desktop\Accepted-Final Articles 2014\Negotiation Journal\Negotiaiton Journal\data and do files\log.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}15 Jan 2015, 16:57:28
{txt}
{com}. 
. //use "C:\Users\mjoshi2\Google Drive\MESH FILES (1)\2013 Working Projects\UCDP Peace Agreement Data\UCDP PeaceAgreement Data\Peace Agreement Data 2012\APSR Revisions\Joshi-Quinn-PA Data Final v. 13March2013.dta", clear 
. 
. //use "C:\Users\mjoshi2\Documents\USEFUL DATA\UCDP Peace Agreement Data\UCDP PeaceAgreement Data\UCDP_PAM.dta", clear
. 
. //Generate Whether the conflict is territorial or government
. gen terri_inco = 1 if inc ==1
{txt}(176 missing values generated)

{com}. replace terri_inc=0 if  terri_inco ==.
{txt}(176 real changes made)

{com}. label variable terri_inco "Territorial Incompatibility if inc ==1"
{txt}
{com}. 
. //Generate Interaction with Territorial Incompatibility and Local government
. 
. replace dyvi05 = . if dyvi05==-99
{txt}(1 real change made, 1 to missing)

{com}. 
. //Categorizing variables for �Are there outstanding issues specified in the agreement?�
. gen outstanding_issue_identified = 1 if out_iss==2 
{txt}(192 missing values generated)

{com}. replace outstanding_issue_identified = 0 if  outstanding_issue_identified==.
{txt}(192 real changes made)

{com}. label variable outstanding_issue_identified "Outstanding issue if out_iss==2"
{txt}
{com}. 
. 
. gen process_agree = 1 if out_iss==1
{txt}(164 missing values generated)

{com}. replace process_agree=0 if process_agree==.
{txt}(164 real changes made)

{com}. label variable process_agree "Process Agreement if out_iss ==1"
{txt}
{com}. 
. gen del_to_commissino = 1 if out_iss==3
{txt}(199 missing values generated)

{com}. replace  del_to_commissino = 0 if  del_to_commissino ==.
{txt}(199 real changes made)

{com}. label variable del_to_commissino "Outstanding Issues delegated to Commission out_iss ==3"
{txt}
{com}. 
. gen new_negotiation = 1 if out_iss==4
{txt}(188 missing values generated)

{com}. replace new_negotiation = 0 if new_negotiation==.
{txt}(188 real changes made)

{com}. 
. gen negotiating_agenda = 0
{txt}
{com}. replace negotiating_agenda = 1 if  out_iss ==5
{txt}(13 real changes made)

{com}. 
. //Generate Individual PA type
. 
. gen full_pa =1 if pa_type==1
{txt}(159 missing values generated)

{com}. replace full_pa=0 if full_pa==.
{txt}(159 real changes made)

{com}. 
. gen partial_pa=0
{txt}
{com}. replace partial_pa=1 if pa_type==2
{txt}(100 real changes made)

{com}. 
. //pwcorr dyvi05  total_prov intarmy   shagov  shaloc terri_inco  regdev locgov   locgov_terriinc  outstanding_issue_identified  process_agree  del_to_commissino negotiating_agenda pko  no_dyad pre_accord  full_pa partial_pa, sig star(0.05)
. 
. //Merge PAM and UCDP with common cases
. gen pam_ucdptotal = total_p
{txt}(20 missing values generated)

{com}. replace pam_ucdptotal = pam if pam!=.
{txt}(32 real changes made)

{com}. 
. //Ways to address endogenity problem - mediation and prior accord 
. //Mediation to address endogenitiy issue
. gen mediation_1 = 0
{txt}
{com}. replace mediation_1 = mediation if mediation!=.
{txt}(130 real changes made)

{com}. 
. //replace  pam_ucdptotal =  total_prov if  pam_ucdptotal==.
. //order  pam_ucdptotal total_prov vi05_2, first
. 
. //creating a variable that has identified outstanding issues all from 1-5 in out_issue at UCDP
. 
. //gen outs_identified = 0
. //replace outs_identified = 1 if out_iss ==0
. 
. gen outstanding_issue = 0
{txt}
{com}. replace outstanding_issue = 1 if out_iss!=0
{txt}(154 real changes made)

{com}. 
. //Summary Stat
. 
. //FINAL FINAL Models -- cluster in country not conflict//this is a new change
. 
. //Mediating Outstanding Issues
. gen med_out = mediation_1* outstanding_issue
{txt}
{com}. 
.  //Generate Log of Population
.  gen  ln_pop = ln(pop_total)
{txt}(20 missing values generated)

{com}. 
.  //Generate log of gdp_percapita 2000 constant USD
. 
. gen ln_gdp_percapita = ln(gdp_percapita_2000)
{txt}(31 missing values generated)

{com}. 
. //Generate log of wardur in days  
. gen ln_duration = log(wardur)
{txt}(20 missing values generated)

{com}. 
. //Generate Conflict Intensity for major wars
. 
. gen major_war = 0
{txt}
{com}. replace major_war=1 if intensity==2
{txt}(108 real changes made)

{com}. 
. //Interaction between cumulative accord and failure
. gen cum_acc_fail =  cum_accord*cum_failure
{txt}(21 missing values generated)

{com}. 
. //Replicate Table 1 : Summary Statistics
. 
. sum dyvi05 pam_ucdptotal total_prov  pa_lead_pa no_dyad pre_accord process terri_inco shaloc intgov pko intarmy mediation_1  ln_duration major_war   polity ethnic_fra  ln_pop ln_gdp infantmort_rate1000  

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 6}dyvi05 {c |}{res}       195          .4    .4911589          0          1
{txt}pam_ucdpto~l {c |}{res}       196    7.612245    7.718951          0         43
{txt}{space 2}total_prov {c |}{res}       196    5.642857    4.008325          0         21
{txt}{space 2}pa_lead_pa {c |}{res}       196     .744898    .4370349          0          1
{txt}{space 5}no_dyad {c |}{res}       196    1.147959    .4788297          1          3
{txt}{hline 13}{c +}{hline 56}
{space 2}pre_accord {c |}{res}       196     .744898    .4370349          0          1
{txt}process_ag~e {c |}{res}       216    .2407407    .4285263          0          1
{txt}{space 2}terri_inco {c |}{res}       216    .1851852      .38935          0          1
{txt}{space 6}shaloc {c |}{res}       196    .0459184    .2098441          0          1
{txt}{space 6}intgov {c |}{res}       196    .1632653    .3705541          0          1
{txt}{hline 13}{c +}{hline 56}
{space 9}pko {c |}{res}       196    .1887755    .3923323          0          1
{txt}{space 5}intarmy {c |}{res}       196    .3469388     .477215          0          1
{txt}{space 1}mediation_1 {c |}{res}       216    .6018519    .4906534          0          1
{txt}{space 1}ln_duration {c |}{res}       196    7.755465    1.273861   2.639057   9.806095
{txt}{space 3}major_war {c |}{res}       216          .5    .5011614          0          1
{txt}{hline 13}{c +}{hline 56}
{space 5}polity2 {c |}{res}       196    .2857143    5.077855         -9         10
{txt}{space 2}ethnic_fra {c |}{res}       196    .6175735    .2485631          0      .9302
{txt}{space 6}ln_pop {c |}{res}       196    16.19467    1.207006   13.21378   20.62903
{txt}ln_gdp_per~a {c |}{res}       185      6.2262    1.256834   3.998384   10.03155
{txt}infantm~1000 {c |}{res}       196    80.73316    40.41374        4.3      161.3
{txt}
{com}. 
. //Table 2
. logit   pa_lead_pa  total_prov pre_accord, cl(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-111.30276}  
Iteration 1:{space 3}log pseudolikelihood = {res:-103.94161}  
Iteration 2:{space 3}log pseudolikelihood = {res:-103.77733}  
Iteration 3:{space 3}log pseudolikelihood = {res:-103.77727}  
Iteration 4:{space 3}log pseudolikelihood = {res:-103.77727}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}       196
{txt}{col 51}Wald chi2({res}2{txt}){col 67}= {res}     13.98
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0009
{txt}Log pseudolikelihood = {res}-103.77727{txt}{col 51}Pseudo R2{col 67}= {res}    0.0676

{txt}{ralign 78:(Std. Err. adjusted for {res:45} clusters in ccode)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  pa_lead_pa{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}total_prov {c |}{col 14}{res}{space 2}-.1018314{col 26}{space 2} .0534002{col 37}{space 1}   -1.91{col 46}{space 3}0.057{col 54}{space 4}-.2064938{col 67}{space 3}  .002831
{txt}{space 2}pre_accord {c |}{col 14}{res}{space 2} 1.064239{col 26}{space 2} .3089095{col 37}{space 1}    3.45{col 46}{space 3}0.001{col 54}{space 4} .4587872{col 67}{space 3}  1.66969
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .9445324{col 26}{space 2}  .405391{col 37}{space 1}    2.33{col 46}{space 3}0.020{col 54}{space 4} .1499807{col 67}{space 3} 1.739084
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit   pa_lead_pa  total_prov no_dyad pre_accord  terri_inco ln_duration major_war polity, cl(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-111.30276}  
Iteration 1:{space 3}log pseudolikelihood = {res:-92.988307}  
Iteration 2:{space 3}log pseudolikelihood = {res:-92.158678}  
Iteration 3:{space 3}log pseudolikelihood = {res:-92.154916}  
Iteration 4:{space 3}log pseudolikelihood = {res:-92.154916}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}       196
{txt}{col 51}Wald chi2({res}7{txt}){col 67}= {res}     33.16
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-92.154916{txt}{col 51}Pseudo R2{col 67}= {res}    0.1720

{txt}{ralign 78:(Std. Err. adjusted for {res:45} clusters in ccode)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  pa_lead_pa{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}total_prov {c |}{col 14}{res}{space 2}-.1376829{col 26}{space 2}  .058629{col 37}{space 1}   -2.35{col 46}{space 3}0.019{col 54}{space 4}-.2525936{col 67}{space 3}-.0227722
{txt}{space 5}no_dyad {c |}{col 14}{res}{space 2} .7538496{col 26}{space 2} .5263826{col 37}{space 1}    1.43{col 46}{space 3}0.152{col 54}{space 4}-.2778413{col 67}{space 3}  1.78554
{txt}{space 2}pre_accord {c |}{col 14}{res}{space 2}  .775167{col 26}{space 2} .3781525{col 37}{space 1}    2.05{col 46}{space 3}0.040{col 54}{space 4} .0340017{col 67}{space 3} 1.516332
{txt}{space 2}terri_inco {c |}{col 14}{res}{space 2}-.8158151{col 26}{space 2} .4602879{col 37}{space 1}   -1.77{col 46}{space 3}0.076{col 54}{space 4}-1.717963{col 67}{space 3} .0863326
{txt}{space 1}ln_duration {c |}{col 14}{res}{space 2} .3810389{col 26}{space 2} .1476873{col 37}{space 1}    2.58{col 46}{space 3}0.010{col 54}{space 4} .0915771{col 67}{space 3} .6705007
{txt}{space 3}major_war {c |}{col 14}{res}{space 2}-.1576152{col 26}{space 2} .4057066{col 37}{space 1}   -0.39{col 46}{space 3}0.698{col 54}{space 4}-.9527856{col 67}{space 3} .6375552
{txt}{space 5}polity2 {c |}{col 14}{res}{space 2}-.1000216{col 26}{space 2} .0418951{col 37}{space 1}   -2.39{col 46}{space 3}0.017{col 54}{space 4}-.1821346{col 67}{space 3}-.0179086
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} -2.02804{col 26}{space 2} 1.369508{col 37}{space 1}   -1.48{col 46}{space 3}0.139{col 54}{space 4}-4.712227{col 67}{space 3} .6561469
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit   pa_lead_pa total_prov no_dyad pre_accord  terri_inco polity mediation_1  ethnic_fra  ln_pop ln_gdp  ln_duration major_war, cl(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-102.63608}  
Iteration 1:{space 3}log pseudolikelihood = {res:-82.870862}  
Iteration 2:{space 3}log pseudolikelihood = {res:-81.530444}  
Iteration 3:{space 3}log pseudolikelihood = {res:-81.514335}  
Iteration 4:{space 3}log pseudolikelihood = {res:-81.514327}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}       185
{txt}{col 51}Wald chi2({res}11{txt}){col 67}= {res}     39.26
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-81.514327{txt}{col 51}Pseudo R2{col 67}= {res}    0.2058

{txt}{ralign 82:(Std. Err. adjusted for {res:41} clusters in ccode)}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}      pa_lead_pa{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}total_prov {c |}{col 18}{res}{space 2}-.1833502{col 30}{space 2} .0722697{col 41}{space 1}   -2.54{col 50}{space 3}0.011{col 58}{space 4}-.3249961{col 71}{space 3}-.0417042
{txt}{space 9}no_dyad {c |}{col 18}{res}{space 2} 1.284558{col 30}{space 2} .7657518{col 41}{space 1}    1.68{col 50}{space 3}0.093{col 58}{space 4}-.2162883{col 71}{space 3} 2.785403
{txt}{space 6}pre_accord {c |}{col 18}{res}{space 2} .6224551{col 30}{space 2} .4012908{col 41}{space 1}    1.55{col 50}{space 3}0.121{col 58}{space 4}-.1640604{col 71}{space 3} 1.408971
{txt}{space 6}terri_inco {c |}{col 18}{res}{space 2}-.3015957{col 30}{space 2} .6348224{col 41}{space 1}   -0.48{col 50}{space 3}0.635{col 58}{space 4}-1.545825{col 71}{space 3} .9426333
{txt}{space 9}polity2 {c |}{col 18}{res}{space 2} -.070801{col 30}{space 2} .0493839{col 41}{space 1}   -1.43{col 50}{space 3}0.152{col 58}{space 4}-.1675916{col 71}{space 3} .0259895
{txt}{space 5}mediation_1 {c |}{col 18}{res}{space 2}-.1587561{col 30}{space 2} .4175869{col 41}{space 1}   -0.38{col 50}{space 3}0.704{col 58}{space 4}-.9772113{col 71}{space 3} .6596991
{txt}{space 6}ethnic_fra {c |}{col 18}{res}{space 2} .5290543{col 30}{space 2} .7936106{col 41}{space 1}    0.67{col 50}{space 3}0.505{col 58}{space 4}-1.026394{col 71}{space 3} 2.084503
{txt}{space 10}ln_pop {c |}{col 18}{res}{space 2}-.1824848{col 30}{space 2} .1335412{col 41}{space 1}   -1.37{col 50}{space 3}0.172{col 58}{space 4}-.4442207{col 71}{space 3} .0792512
{txt}ln_gdp_percapita {c |}{col 18}{res}{space 2}-.4297003{col 30}{space 2} .1860273{col 41}{space 1}   -2.31{col 50}{space 3}0.021{col 58}{space 4} -.794307{col 71}{space 3}-.0650935
{txt}{space 5}ln_duration {c |}{col 18}{res}{space 2} .5778037{col 30}{space 2} .1971057{col 41}{space 1}    2.93{col 50}{space 3}0.003{col 58}{space 4} .1914836{col 71}{space 3} .9641238
{txt}{space 7}major_war {c |}{col 18}{res}{space 2}-.0908391{col 30}{space 2} .4584952{col 41}{space 1}   -0.20{col 50}{space 3}0.843{col 58}{space 4}-.9894732{col 71}{space 3} .8077949
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} 1.612739{col 30}{space 2} 2.077548{col 41}{space 1}    0.78{col 50}{space 3}0.438{col 58}{space 4}-2.459182{col 71}{space 3} 5.684659
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit   pa_lead_pa total_prov no_dyad pre_accord process terri_inco polity mediation_1  ethnic_fra  ln_pop infantmort_rate1000  ln_duration major_war, cl(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-111.30276}  
Iteration 1:{space 3}log pseudolikelihood = {res:-86.623959}  
Iteration 2:{space 3}log pseudolikelihood = {res:-84.217151}  
Iteration 3:{space 3}log pseudolikelihood = {res: -84.12162}  
Iteration 4:{space 3}log pseudolikelihood = {res:-84.121235}  
Iteration 5:{space 3}log pseudolikelihood = {res:-84.121235}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}       196
{txt}{col 51}Wald chi2({res}12{txt}){col 67}= {res}     42.47
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-84.121235{txt}{col 51}Pseudo R2{col 67}= {res}    0.2442

{txt}{ralign 85:(Std. Err. adjusted for {res:45} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}         pa_lead_pa{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}total_prov {c |}{col 21}{res}{space 2}-.1365027{col 33}{space 2} .0692292{col 44}{space 1}   -1.97{col 53}{space 3}0.049{col 61}{space 4}-.2721894{col 74}{space 3}-.0008161
{txt}{space 12}no_dyad {c |}{col 21}{res}{space 2} .7728064{col 33}{space 2} .5457762{col 44}{space 1}    1.42{col 53}{space 3}0.157{col 61}{space 4}-.2968952{col 74}{space 3} 1.842508
{txt}{space 9}pre_accord {c |}{col 21}{res}{space 2} .3955801{col 33}{space 2} .3786725{col 44}{space 1}    1.04{col 53}{space 3}0.296{col 61}{space 4}-.3466043{col 74}{space 3} 1.137764
{txt}{space 6}process_agree {c |}{col 21}{res}{space 2} 1.900951{col 33}{space 2} .6381919{col 44}{space 1}    2.98{col 53}{space 3}0.003{col 61}{space 4} .6501178{col 74}{space 3} 3.151784
{txt}{space 9}terri_inco {c |}{col 21}{res}{space 2} .0945582{col 33}{space 2} .6768353{col 44}{space 1}    0.14{col 53}{space 3}0.889{col 61}{space 4}-1.232015{col 74}{space 3} 1.421131
{txt}{space 12}polity2 {c |}{col 21}{res}{space 2}-.0495887{col 33}{space 2} .0448404{col 44}{space 1}   -1.11{col 53}{space 3}0.269{col 61}{space 4}-.1374742{col 74}{space 3} .0382969
{txt}{space 8}mediation_1 {c |}{col 21}{res}{space 2} -.009315{col 33}{space 2}  .453911{col 44}{space 1}   -0.02{col 53}{space 3}0.984{col 61}{space 4}-.8989642{col 74}{space 3} .8803343
{txt}{space 9}ethnic_fra {c |}{col 21}{res}{space 2} .5744621{col 33}{space 2} .8555199{col 44}{space 1}    0.67{col 53}{space 3}0.502{col 61}{space 4}-1.102326{col 74}{space 3}  2.25125
{txt}{space 13}ln_pop {c |}{col 21}{res}{space 2}-.0938978{col 33}{space 2}  .125154{col 44}{space 1}   -0.75{col 53}{space 3}0.453{col 61}{space 4}-.3391952{col 74}{space 3} .1513996
{txt}infantmort_rate1000 {c |}{col 21}{res}{space 2} .0183075{col 33}{space 2} .0059952{col 44}{space 1}    3.05{col 53}{space 3}0.002{col 61}{space 4}  .006557{col 74}{space 3} .0300579
{txt}{space 8}ln_duration {c |}{col 21}{res}{space 2} .4903867{col 33}{space 2} .1923933{col 44}{space 1}    2.55{col 53}{space 3}0.011{col 61}{space 4} .1133027{col 74}{space 3} .8674707
{txt}{space 10}major_war {c |}{col 21}{res}{space 2} .0288579{col 33}{space 2} .4013579{col 44}{space 1}    0.07{col 53}{space 3}0.943{col 61}{space 4}-.7577891{col 74}{space 3} .8155048
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-3.508887{col 33}{space 2} 1.819912{col 44}{space 1}   -1.93{col 53}{space 3}0.054{col 61}{space 4}-7.075848{col 74}{space 3} .0580747
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. //Replicate Model 2, 3 and 4 with PAM data
. logit  pa_lead_pa  pam_ucdptotal no_dyad pre_accord  terri_inco ln_duration major_war polity, cl(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-111.30276}  
Iteration 1:{space 3}log pseudolikelihood = {res:-89.287131}  
Iteration 2:{space 3}log pseudolikelihood = {res:-88.297481}  
Iteration 3:{space 3}log pseudolikelihood = {res:-88.293857}  
Iteration 4:{space 3}log pseudolikelihood = {res:-88.293857}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}       196
{txt}{col 51}Wald chi2({res}7{txt}){col 67}= {res}     33.34
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-88.293857{txt}{col 51}Pseudo R2{col 67}= {res}    0.2067

{txt}{ralign 79:(Std. Err. adjusted for {res:45} clusters in ccode)}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}   pa_lead_pa{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
pam_ucdptotal {c |}{col 15}{res}{space 2}-.0894964{col 27}{space 2} .0288246{col 38}{space 1}   -3.10{col 47}{space 3}0.002{col 55}{space 4}-.1459916{col 68}{space 3}-.0330012
{txt}{space 6}no_dyad {c |}{col 15}{res}{space 2} .8134456{col 27}{space 2} .5350989{col 38}{space 1}    1.52{col 47}{space 3}0.128{col 55}{space 4}-.2353291{col 68}{space 3}  1.86222
{txt}{space 3}pre_accord {c |}{col 15}{res}{space 2} .8238281{col 27}{space 2} .4070476{col 38}{space 1}    2.02{col 47}{space 3}0.043{col 55}{space 4} .0260295{col 68}{space 3} 1.621627
{txt}{space 3}terri_inco {c |}{col 15}{res}{space 2}-.7664191{col 27}{space 2}   .48381{col 38}{space 1}   -1.58{col 47}{space 3}0.113{col 55}{space 4}-1.714669{col 68}{space 3}  .181831
{txt}{space 2}ln_duration {c |}{col 15}{res}{space 2} .4383471{col 27}{space 2} .1552255{col 38}{space 1}    2.82{col 47}{space 3}0.005{col 55}{space 4} .1341107{col 68}{space 3} .7425835
{txt}{space 4}major_war {c |}{col 15}{res}{space 2}-.2877245{col 27}{space 2} .3959476{col 38}{space 1}   -0.73{col 47}{space 3}0.467{col 55}{space 4}-1.063767{col 68}{space 3} .4883184
{txt}{space 6}polity2 {c |}{col 15}{res}{space 2}-.0925446{col 27}{space 2} .0373606{col 38}{space 1}   -2.48{col 47}{space 3}0.013{col 55}{space 4}  -.16577{col 68}{space 3}-.0193192
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}-2.583138{col 27}{space 2} 1.347928{col 38}{space 1}   -1.92{col 47}{space 3}0.055{col 55}{space 4}-5.225029{col 68}{space 3} .0587528
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit   pa_lead_pa pam_ucdptotal no_dyad pre_accord  terri_inco polity mediation_1  ethnic_fra  ln_pop ln_gdp  ln_duration major_war, cl(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-102.63608}  
Iteration 1:{space 3}log pseudolikelihood = {res:-80.483135}  
Iteration 2:{space 3}log pseudolikelihood = {res:-79.140599}  
Iteration 3:{space 3}log pseudolikelihood = {res:-79.125566}  
Iteration 4:{space 3}log pseudolikelihood = {res:-79.125555}  
Iteration 5:{space 3}log pseudolikelihood = {res:-79.125555}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}       185
{txt}{col 51}Wald chi2({res}11{txt}){col 67}= {res}     35.13
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0002
{txt}Log pseudolikelihood = {res}-79.125555{txt}{col 51}Pseudo R2{col 67}= {res}    0.2291

{txt}{ralign 82:(Std. Err. adjusted for {res:41} clusters in ccode)}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}      pa_lead_pa{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}pam_ucdptotal {c |}{col 18}{res}{space 2}-.0968081{col 30}{space 2} .0322212{col 41}{space 1}   -3.00{col 50}{space 3}0.003{col 58}{space 4}-.1599605{col 71}{space 3}-.0336556
{txt}{space 9}no_dyad {c |}{col 18}{res}{space 2} 1.251084{col 30}{space 2} .7217133{col 41}{space 1}    1.73{col 50}{space 3}0.083{col 58}{space 4}-.1634483{col 71}{space 3} 2.665616
{txt}{space 6}pre_accord {c |}{col 18}{res}{space 2} .7246315{col 30}{space 2} .4295937{col 41}{space 1}    1.69{col 50}{space 3}0.092{col 58}{space 4}-.1173567{col 71}{space 3}  1.56662
{txt}{space 6}terri_inco {c |}{col 18}{res}{space 2}-.4189684{col 30}{space 2} .6627175{col 41}{space 1}   -0.63{col 50}{space 3}0.527{col 58}{space 4}-1.717871{col 71}{space 3} .8799341
{txt}{space 9}polity2 {c |}{col 18}{res}{space 2}-.0645712{col 30}{space 2} .0435289{col 41}{space 1}   -1.48{col 50}{space 3}0.138{col 58}{space 4}-.1498862{col 71}{space 3} .0207438
{txt}{space 5}mediation_1 {c |}{col 18}{res}{space 2}-.0648374{col 30}{space 2}  .441579{col 41}{space 1}   -0.15{col 50}{space 3}0.883{col 58}{space 4}-.9303164{col 71}{space 3} .8006415
{txt}{space 6}ethnic_fra {c |}{col 18}{res}{space 2} .3381275{col 30}{space 2} .8115566{col 41}{space 1}    0.42{col 50}{space 3}0.677{col 58}{space 4}-1.252494{col 71}{space 3} 1.928749
{txt}{space 10}ln_pop {c |}{col 18}{res}{space 2}-.1427705{col 30}{space 2} .1349395{col 41}{space 1}   -1.06{col 50}{space 3}0.290{col 58}{space 4} -.407247{col 71}{space 3}  .121706
{txt}ln_gdp_percapita {c |}{col 18}{res}{space 2}-.3108101{col 30}{space 2} .1843196{col 41}{space 1}   -1.69{col 50}{space 3}0.092{col 58}{space 4}-.6720699{col 71}{space 3} .0504496
{txt}{space 5}ln_duration {c |}{col 18}{res}{space 2} .5875122{col 30}{space 2} .2053218{col 41}{space 1}    2.86{col 50}{space 3}0.004{col 58}{space 4} .1850888{col 71}{space 3} .9899355
{txt}{space 7}major_war {c |}{col 18}{res}{space 2}-.2249039{col 30}{space 2} .4530367{col 41}{space 1}   -0.50{col 50}{space 3}0.620{col 58}{space 4}-1.112839{col 71}{space 3} .6630317
{txt}{space 11}_cons {c |}{col 18}{res}{space 2}-.0288636{col 30}{space 2} 2.118606{col 41}{space 1}   -0.01{col 50}{space 3}0.989{col 58}{space 4}-4.181255{col 71}{space 3} 4.123528
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit   pa_lead_pa pam_ucdptotal no_dyad pre_accord process terri_inco polity mediation_1  ethnic_fra  ln_pop infantmort_rate1000  ln_duration major_war, cl(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-111.30276}  
Iteration 1:{space 3}log pseudolikelihood = {res: -84.48948}  
Iteration 2:{space 3}log pseudolikelihood = {res:-82.116306}  
Iteration 3:{space 3}log pseudolikelihood = {res:-82.023067}  
Iteration 4:{space 3}log pseudolikelihood = {res:-82.022759}  
Iteration 5:{space 3}log pseudolikelihood = {res:-82.022759}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}       196
{txt}{col 51}Wald chi2({res}12{txt}){col 67}= {res}     34.47
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0006
{txt}Log pseudolikelihood = {res}-82.022759{txt}{col 51}Pseudo R2{col 67}= {res}    0.2631

{txt}{ralign 85:(Std. Err. adjusted for {res:45} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}         pa_lead_pa{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}pam_ucdptotal {c |}{col 21}{res}{space 2}-.0789614{col 33}{space 2} .0309044{col 44}{space 1}   -2.56{col 53}{space 3}0.011{col 61}{space 4}-.1395328{col 74}{space 3}  -.01839
{txt}{space 12}no_dyad {c |}{col 21}{res}{space 2} .8057377{col 33}{space 2} .5231896{col 44}{space 1}    1.54{col 53}{space 3}0.124{col 61}{space 4}-.2196951{col 74}{space 3}  1.83117
{txt}{space 9}pre_accord {c |}{col 21}{res}{space 2} .4825616{col 33}{space 2} .3951816{col 44}{space 1}    1.22{col 53}{space 3}0.222{col 61}{space 4}-.2919801{col 74}{space 3} 1.257103
{txt}{space 6}process_agree {c |}{col 21}{res}{space 2}  1.80012{col 33}{space 2}  .681863{col 44}{space 1}    2.64{col 53}{space 3}0.008{col 61}{space 4} .4636929{col 74}{space 3} 3.136547
{txt}{space 9}terri_inco {c |}{col 21}{res}{space 2}-.0481219{col 33}{space 2} .6911115{col 44}{space 1}   -0.07{col 53}{space 3}0.944{col 61}{space 4}-1.402675{col 74}{space 3} 1.306432
{txt}{space 12}polity2 {c |}{col 21}{res}{space 2} -.049754{col 33}{space 2} .0405539{col 44}{space 1}   -1.23{col 53}{space 3}0.220{col 61}{space 4}-.1292382{col 74}{space 3} .0297303
{txt}{space 8}mediation_1 {c |}{col 21}{res}{space 2} .0547021{col 33}{space 2} .4753808{col 44}{space 1}    0.12{col 53}{space 3}0.908{col 61}{space 4} -.877027{col 74}{space 3} .9864313
{txt}{space 9}ethnic_fra {c |}{col 21}{res}{space 2} .4486149{col 33}{space 2}  .825134{col 44}{space 1}    0.54{col 53}{space 3}0.587{col 61}{space 4}-1.168618{col 74}{space 3} 2.065848
{txt}{space 13}ln_pop {c |}{col 21}{res}{space 2}-.0758331{col 33}{space 2} .1213191{col 44}{space 1}   -0.63{col 53}{space 3}0.532{col 61}{space 4}-.3136142{col 74}{space 3}  .161948
{txt}infantmort_rate1000 {c |}{col 21}{res}{space 2} .0150745{col 33}{space 2} .0057094{col 44}{space 1}    2.64{col 53}{space 3}0.008{col 61}{space 4} .0038843{col 74}{space 3} .0262647
{txt}{space 8}ln_duration {c |}{col 21}{res}{space 2} .5314426{col 33}{space 2} .1888791{col 44}{space 1}    2.81{col 53}{space 3}0.005{col 61}{space 4} .1612464{col 74}{space 3} .9016388
{txt}{space 10}major_war {c |}{col 21}{res}{space 2}-.1485076{col 33}{space 2} .3861404{col 44}{space 1}   -0.38{col 53}{space 3}0.701{col 61}{space 4} -.905329{col 74}{space 3} .6083137
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-3.940087{col 33}{space 2}  1.85795{col 44}{space 1}   -2.12{col 53}{space 3}0.034{col 61}{space 4}-7.581602{col 74}{space 3}-.2985709
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. //Table 3, Model 1, 2, 3, 4, 5, 6 and 7
.  
. 
. logit   dyvi05  total_p pre_accord, cl(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-131.23728}  
Iteration 1:{space 3}log pseudolikelihood = {res:-125.27724}  
Iteration 2:{space 3}log pseudolikelihood = {res:-125.24638}  
Iteration 3:{space 3}log pseudolikelihood = {res:-125.24638}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}       195
{txt}{col 51}Wald chi2({res}2{txt}){col 67}= {res}      7.79
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0203
{txt}Log pseudolikelihood = {res}-125.24638{txt}{col 51}Pseudo R2{col 67}= {res}    0.0456

{txt}{ralign 78:(Std. Err. adjusted for {res:45} clusters in ccode)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      dyvi05{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}total_prov {c |}{col 14}{res}{space 2}-.1275918{col 26}{space 2} .0527258{col 37}{space 1}   -2.42{col 46}{space 3}0.016{col 54}{space 4}-.2309325{col 67}{space 3}-.0242511
{txt}{space 2}pre_accord {c |}{col 14}{res}{space 2}-.5437539{col 26}{space 2} .3574805{col 37}{space 1}   -1.52{col 46}{space 3}0.128{col 54}{space 4}-1.244403{col 67}{space 3}  .156895
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .6831407{col 26}{space 2} .4209455{col 37}{space 1}    1.62{col 46}{space 3}0.105{col 54}{space 4}-.1418974{col 67}{space 3} 1.508179
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit   dyvi05  total_p no_dyad pre_accord  terri_inco ln_duration major_war polity, cl(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-131.23728}  
Iteration 1:{space 3}log pseudolikelihood = {res:-113.75275}  
Iteration 2:{space 3}log pseudolikelihood = {res:-113.49585}  
Iteration 3:{space 3}log pseudolikelihood = {res:-113.49516}  
Iteration 4:{space 3}log pseudolikelihood = {res:-113.49516}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}       195
{txt}{col 51}Wald chi2({res}7{txt}){col 67}= {res}     23.50
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0014
{txt}Log pseudolikelihood = {res}-113.49516{txt}{col 51}Pseudo R2{col 67}= {res}    0.1352

{txt}{ralign 78:(Std. Err. adjusted for {res:45} clusters in ccode)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      dyvi05{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}total_prov {c |}{col 14}{res}{space 2} -.184489{col 26}{space 2} .0568731{col 37}{space 1}   -3.24{col 46}{space 3}0.001{col 54}{space 4}-.2959583{col 67}{space 3}-.0730197
{txt}{space 5}no_dyad {c |}{col 14}{res}{space 2} .9491892{col 26}{space 2} .5928573{col 37}{space 1}    1.60{col 46}{space 3}0.109{col 54}{space 4}-.2127897{col 67}{space 3} 2.111168
{txt}{space 2}pre_accord {c |}{col 14}{res}{space 2}-.9334631{col 26}{space 2} .4140157{col 37}{space 1}   -2.25{col 46}{space 3}0.024{col 54}{space 4}-1.744919{col 67}{space 3}-.1220072
{txt}{space 2}terri_inco {c |}{col 14}{res}{space 2}-.2542405{col 26}{space 2} .5415978{col 37}{space 1}   -0.47{col 46}{space 3}0.639{col 54}{space 4}-1.315753{col 67}{space 3} .8072717
{txt}{space 1}ln_duration {c |}{col 14}{res}{space 2} .1787134{col 26}{space 2} .1727342{col 37}{space 1}    1.03{col 46}{space 3}0.301{col 54}{space 4}-.1598394{col 67}{space 3} .5172662
{txt}{space 3}major_war {c |}{col 14}{res}{space 2} .7359085{col 26}{space 2} .3453814{col 37}{space 1}    2.13{col 46}{space 3}0.033{col 54}{space 4} .0589734{col 67}{space 3} 1.412844
{txt}{space 5}polity2 {c |}{col 14}{res}{space 2}-.0727245{col 26}{space 2}  .051989{col 37}{space 1}   -1.40{col 46}{space 3}0.162{col 54}{space 4}-.1746211{col 67}{space 3} .0291722
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-1.577163{col 26}{space 2} 1.567089{col 37}{space 1}   -1.01{col 46}{space 3}0.314{col 54}{space 4}  -4.6486{col 67}{space 3} 1.494274
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit   dyvi05 total_p no_dyad pre_accord  terri_inco polity mediation_1  ethnic_fra  ln_pop ln_gdp  ln_duration major_war, cl(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-123.58679}  
Iteration 1:{space 3}log pseudolikelihood = {res:-102.85626}  
Iteration 2:{space 3}log pseudolikelihood = {res:-102.40543}  
Iteration 3:{space 3}log pseudolikelihood = {res:-102.40432}  
Iteration 4:{space 3}log pseudolikelihood = {res:-102.40432}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}       184
{txt}{col 51}Wald chi2({res}11{txt}){col 67}= {res}     32.13
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0007
{txt}Log pseudolikelihood = {res}-102.40432{txt}{col 51}Pseudo R2{col 67}= {res}    0.1714

{txt}{ralign 82:(Std. Err. adjusted for {res:41} clusters in ccode)}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}          dyvi05{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}total_prov {c |}{col 18}{res}{space 2}-.2053262{col 30}{space 2} .0660469{col 41}{space 1}   -3.11{col 50}{space 3}0.002{col 58}{space 4}-.3347757{col 71}{space 3}-.0758767
{txt}{space 9}no_dyad {c |}{col 18}{res}{space 2} .5986619{col 30}{space 2} .6024226{col 41}{space 1}    0.99{col 50}{space 3}0.320{col 58}{space 4}-.5820647{col 71}{space 3} 1.779389
{txt}{space 6}pre_accord {c |}{col 18}{res}{space 2}-.9391852{col 30}{space 2} .4566627{col 41}{space 1}   -2.06{col 50}{space 3}0.040{col 58}{space 4}-1.834228{col 71}{space 3}-.0441427
{txt}{space 6}terri_inco {c |}{col 18}{res}{space 2}-.5963664{col 30}{space 2} .7083628{col 41}{space 1}   -0.84{col 50}{space 3}0.400{col 58}{space 4}-1.984732{col 71}{space 3} .7919991
{txt}{space 9}polity2 {c |}{col 18}{res}{space 2} -.054452{col 30}{space 2} .0772365{col 41}{space 1}   -0.71{col 50}{space 3}0.481{col 58}{space 4}-.2058327{col 71}{space 3} .0969288
{txt}{space 5}mediation_1 {c |}{col 18}{res}{space 2} 1.302846{col 30}{space 2} .5265038{col 41}{space 1}    2.47{col 50}{space 3}0.013{col 58}{space 4} .2709175{col 71}{space 3} 2.334775
{txt}{space 6}ethnic_fra {c |}{col 18}{res}{space 2}-.3878056{col 30}{space 2} 1.191221{col 41}{space 1}   -0.33{col 50}{space 3}0.745{col 58}{space 4}-2.722556{col 71}{space 3} 1.946945
{txt}{space 10}ln_pop {c |}{col 18}{res}{space 2} .1319293{col 30}{space 2} .2132586{col 41}{space 1}    0.62{col 50}{space 3}0.536{col 58}{space 4}-.2860499{col 71}{space 3} .5499085
{txt}ln_gdp_percapita {c |}{col 18}{res}{space 2} .1355235{col 30}{space 2} .2513562{col 41}{space 1}    0.54{col 50}{space 3}0.590{col 58}{space 4}-.3571256{col 71}{space 3} .6281726
{txt}{space 5}ln_duration {c |}{col 18}{res}{space 2}-.0268958{col 30}{space 2} .2216738{col 41}{space 1}   -0.12{col 50}{space 3}0.903{col 58}{space 4}-.4613684{col 71}{space 3} .4075768
{txt}{space 7}major_war {c |}{col 18}{res}{space 2} 1.080532{col 30}{space 2} .3883382{col 41}{space 1}    2.78{col 50}{space 3}0.005{col 58}{space 4} .3194031{col 71}{space 3} 1.841661
{txt}{space 11}_cons {c |}{col 18}{res}{space 2}-3.226518{col 30}{space 2}  3.45426{col 41}{space 1}   -0.93{col 50}{space 3}0.350{col 58}{space 4}-9.996743{col 71}{space 3} 3.543707
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit   dyvi05 total_p no_dyad pre_accord  terri_inco polity mediation_1  ethnic_fra  ln_pop infantmort_rate1000  ln_duration major_war, cl(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-131.23728}  
Iteration 1:{space 3}log pseudolikelihood = {res:-107.25356}  
Iteration 2:{space 3}log pseudolikelihood = {res:-106.82265}  
Iteration 3:{space 3}log pseudolikelihood = {res:-106.82173}  
Iteration 4:{space 3}log pseudolikelihood = {res:-106.82173}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}       195
{txt}{col 51}Wald chi2({res}11{txt}){col 67}= {res}     37.97
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0001
{txt}Log pseudolikelihood = {res}-106.82173{txt}{col 51}Pseudo R2{col 67}= {res}    0.1860

{txt}{ralign 85:(Std. Err. adjusted for {res:45} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             dyvi05{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}total_prov {c |}{col 21}{res}{space 2}-.2439496{col 33}{space 2} .0595916{col 44}{space 1}   -4.09{col 53}{space 3}0.000{col 61}{space 4} -.360747{col 74}{space 3}-.1271522
{txt}{space 12}no_dyad {c |}{col 21}{res}{space 2} .8001231{col 33}{space 2} .6872247{col 44}{space 1}    1.16{col 53}{space 3}0.244{col 61}{space 4}-.5468125{col 74}{space 3} 2.147059
{txt}{space 9}pre_accord {c |}{col 21}{res}{space 2} -1.19011{col 33}{space 2} .4826995{col 44}{space 1}   -2.47{col 53}{space 3}0.014{col 61}{space 4}-2.136184{col 74}{space 3}-.2440366
{txt}{space 9}terri_inco {c |}{col 21}{res}{space 2}-.1528061{col 33}{space 2} .6612423{col 44}{space 1}   -0.23{col 53}{space 3}0.817{col 61}{space 4}-1.448817{col 74}{space 3} 1.143205
{txt}{space 12}polity2 {c |}{col 21}{res}{space 2}-.0185839{col 33}{space 2}  .068633{col 44}{space 1}   -0.27{col 53}{space 3}0.787{col 61}{space 4}-.1531021{col 74}{space 3} .1159343
{txt}{space 8}mediation_1 {c |}{col 21}{res}{space 2}  .919822{col 33}{space 2}  .482762{col 44}{space 1}    1.91{col 53}{space 3}0.057{col 61}{space 4} -.026374{col 74}{space 3} 1.866018
{txt}{space 9}ethnic_fra {c |}{col 21}{res}{space 2} -.840554{col 33}{space 2} 1.141071{col 44}{space 1}   -0.74{col 53}{space 3}0.461{col 61}{space 4}-3.077013{col 74}{space 3} 1.395905
{txt}{space 13}ln_pop {c |}{col 21}{res}{space 2} .1467166{col 33}{space 2} .1961988{col 44}{space 1}    0.75{col 53}{space 3}0.455{col 61}{space 4} -.237826{col 74}{space 3} .5312593
{txt}infantmort_rate1000 {c |}{col 21}{res}{space 2} .0166272{col 33}{space 2} .0068692{col 44}{space 1}    2.42{col 53}{space 3}0.015{col 61}{space 4} .0031638{col 74}{space 3} .0300906
{txt}{space 8}ln_duration {c |}{col 21}{res}{space 2} .2661566{col 33}{space 2} .2272801{col 44}{space 1}    1.17{col 53}{space 3}0.242{col 61}{space 4}-.1793043{col 74}{space 3} .7116174
{txt}{space 10}major_war {c |}{col 21}{res}{space 2} 1.041485{col 33}{space 2} .3708285{col 44}{space 1}    2.81{col 53}{space 3}0.005{col 61}{space 4} .3146746{col 74}{space 3} 1.768296
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-5.599101{col 33}{space 2} 3.829106{col 44}{space 1}   -1.46{col 53}{space 3}0.144{col 61}{space 4}-13.10401{col 74}{space 3}  1.90581
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. //Replicate Model 2, 3 and 4 with PAM data
. logit   dyvi05  pam_ucdptotal no_dyad pre_accord  terri_inco ln_duration major_war polity, cl(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-131.23728}  
Iteration 1:{space 3}log pseudolikelihood = {res:-113.91352}  
Iteration 2:{space 3}log pseudolikelihood = {res:-113.36883}  
Iteration 3:{space 3}log pseudolikelihood = {res:-113.36667}  
Iteration 4:{space 3}log pseudolikelihood = {res:-113.36667}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}       195
{txt}{col 51}Wald chi2({res}7{txt}){col 67}= {res}     19.48
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0068
{txt}Log pseudolikelihood = {res}-113.36667{txt}{col 51}Pseudo R2{col 67}= {res}    0.1362

{txt}{ralign 79:(Std. Err. adjusted for {res:45} clusters in ccode)}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}       dyvi05{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
pam_ucdptotal {c |}{col 15}{res}{space 2}-.1037047{col 27}{space 2} .0362099{col 38}{space 1}   -2.86{col 47}{space 3}0.004{col 55}{space 4}-.1746748{col 68}{space 3}-.0327345
{txt}{space 6}no_dyad {c |}{col 15}{res}{space 2} .9310871{col 27}{space 2}  .640626{col 38}{space 1}    1.45{col 47}{space 3}0.146{col 55}{space 4}-.3245167{col 68}{space 3} 2.186691
{txt}{space 3}pre_accord {c |}{col 15}{res}{space 2}-.9680153{col 27}{space 2} .4155849{col 38}{space 1}   -2.33{col 47}{space 3}0.020{col 55}{space 4}-1.782547{col 68}{space 3}-.1534839
{txt}{space 3}terri_inco {c |}{col 15}{res}{space 2}-.2619054{col 27}{space 2} .5402005{col 38}{space 1}   -0.48{col 47}{space 3}0.628{col 55}{space 4}-1.320679{col 68}{space 3} .7968681
{txt}{space 2}ln_duration {c |}{col 15}{res}{space 2} .2276523{col 27}{space 2} .1831789{col 38}{space 1}    1.24{col 47}{space 3}0.214{col 55}{space 4}-.1313717{col 68}{space 3} .5866762
{txt}{space 4}major_war {c |}{col 15}{res}{space 2} .6421297{col 27}{space 2} .3614329{col 38}{space 1}    1.78{col 47}{space 3}0.076{col 55}{space 4}-.0662658{col 68}{space 3} 1.350525
{txt}{space 6}polity2 {c |}{col 15}{res}{space 2}-.0601378{col 27}{space 2} .0499023{col 38}{space 1}   -1.21{col 47}{space 3}0.228{col 55}{space 4}-.1579445{col 68}{space 3}  .037669
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}-2.147844{col 27}{space 2} 1.589974{col 38}{space 1}   -1.35{col 47}{space 3}0.177{col 55}{space 4}-5.264136{col 68}{space 3} .9684486
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit   dyvi05 pam_ucdptotal no_dyad pre_accord  terri_inco polity mediation_1  ethnic_fra  ln_pop ln_gdp  ln_duration major_war, cl(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-123.58679}  
Iteration 1:{space 3}log pseudolikelihood = {res:-102.51658}  
Iteration 2:{space 3}log pseudolikelihood = {res:-101.82046}  
Iteration 3:{space 3}log pseudolikelihood = {res:-101.81765}  
Iteration 4:{space 3}log pseudolikelihood = {res:-101.81765}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}       184
{txt}{col 51}Wald chi2({res}11{txt}){col 67}= {res}     26.38
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0057
{txt}Log pseudolikelihood = {res}-101.81765{txt}{col 51}Pseudo R2{col 67}= {res}    0.1761

{txt}{ralign 82:(Std. Err. adjusted for {res:41} clusters in ccode)}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}          dyvi05{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}pam_ucdptotal {c |}{col 18}{res}{space 2}-.1090458{col 30}{space 2} .0386333{col 41}{space 1}   -2.82{col 50}{space 3}0.005{col 58}{space 4}-.1847657{col 71}{space 3}-.0333258
{txt}{space 9}no_dyad {c |}{col 18}{res}{space 2} .5447449{col 30}{space 2} .6129646{col 41}{space 1}    0.89{col 50}{space 3}0.374{col 58}{space 4}-.6566436{col 71}{space 3} 1.746133
{txt}{space 6}pre_accord {c |}{col 18}{res}{space 2}-1.018629{col 30}{space 2} .4726547{col 41}{space 1}   -2.16{col 50}{space 3}0.031{col 58}{space 4}-1.945015{col 71}{space 3}-.0922424
{txt}{space 6}terri_inco {c |}{col 18}{res}{space 2}-.7846947{col 30}{space 2}  .755436{col 41}{space 1}   -1.04{col 50}{space 3}0.299{col 58}{space 4}-2.265322{col 71}{space 3} .6959328
{txt}{space 9}polity2 {c |}{col 18}{res}{space 2}-.0541699{col 30}{space 2} .0745654{col 41}{space 1}   -0.73{col 50}{space 3}0.468{col 58}{space 4}-.2003153{col 71}{space 3} .0919756
{txt}{space 5}mediation_1 {c |}{col 18}{res}{space 2} 1.357861{col 30}{space 2} .4965156{col 41}{space 1}    2.73{col 50}{space 3}0.006{col 58}{space 4}  .384708{col 71}{space 3} 2.331013
{txt}{space 6}ethnic_fra {c |}{col 18}{res}{space 2}-.6010369{col 30}{space 2} 1.229705{col 41}{space 1}   -0.49{col 50}{space 3}0.625{col 58}{space 4}-3.011215{col 71}{space 3} 1.809141
{txt}{space 10}ln_pop {c |}{col 18}{res}{space 2} .1474379{col 30}{space 2} .2234264{col 41}{space 1}    0.66{col 50}{space 3}0.509{col 58}{space 4}-.2904698{col 71}{space 3} .5853455
{txt}ln_gdp_percapita {c |}{col 18}{res}{space 2} .2771621{col 30}{space 2} .2537278{col 41}{space 1}    1.09{col 50}{space 3}0.275{col 58}{space 4}-.2201352{col 71}{space 3} .7744595
{txt}{space 5}ln_duration {c |}{col 18}{res}{space 2}-.0265293{col 30}{space 2} .2314407{col 41}{space 1}   -0.11{col 50}{space 3}0.909{col 58}{space 4}-.4801447{col 71}{space 3} .4270861
{txt}{space 7}major_war {c |}{col 18}{res}{space 2} 1.005863{col 30}{space 2} .4019671{col 41}{space 1}    2.50{col 50}{space 3}0.012{col 58}{space 4}  .218022{col 71}{space 3} 1.793704
{txt}{space 11}_cons {c |}{col 18}{res}{space 2}-4.421295{col 30}{space 2} 3.613288{col 41}{space 1}   -1.22{col 50}{space 3}0.221{col 58}{space 4}-11.50321{col 71}{space 3}  2.66062
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit   dyvi05 pam_ucdptotal no_dyad pre_accord  terri_inco polity mediation_1  ethnic_fra  ln_pop infantmort_rate1000  ln_duration major_war, cl((ccode)
{err}) required
{txt}{search r(100):r(100);}

end of do-file

{search r(100):r(100);}

{com}. do "C:\Users\MJOSHI~1.DSS\AppData\Local\Temp\STD06000000.tmp"
{txt}
{com}. logit   dyvi05 pam_ucdptotal no_dyad pre_accord  terri_inco polity mediation_1  ethnic_fra  ln_pop infantmort_rate1000  ln_duration major_war, cl(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-131.23728}  
Iteration 1:{space 3}log pseudolikelihood = {res:-108.47707}  
Iteration 2:{space 3}log pseudolikelihood = {res:-107.66891}  
Iteration 3:{space 3}log pseudolikelihood = {res:-107.66157}  
Iteration 4:{space 3}log pseudolikelihood = {res:-107.66157}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}       195
{txt}{col 51}Wald chi2({res}11{txt}){col 67}= {res}     34.56
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0003
{txt}Log pseudolikelihood = {res}-107.66157{txt}{col 51}Pseudo R2{col 67}= {res}    0.1796

{txt}{ralign 85:(Std. Err. adjusted for {res:45} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             dyvi05{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}pam_ucdptotal {c |}{col 21}{res}{space 2}-.1300308{col 33}{space 2} .0409884{col 44}{space 1}   -3.17{col 53}{space 3}0.002{col 61}{space 4}-.2103666{col 74}{space 3} -.049695
{txt}{space 12}no_dyad {c |}{col 21}{res}{space 2} .8195771{col 33}{space 2} .7401455{col 44}{space 1}    1.11{col 53}{space 3}0.268{col 61}{space 4}-.6310814{col 74}{space 3} 2.270236
{txt}{space 9}pre_accord {c |}{col 21}{res}{space 2}-1.230839{col 33}{space 2} .4878598{col 44}{space 1}   -2.52{col 53}{space 3}0.012{col 61}{space 4}-2.187027{col 74}{space 3}-.2746516
{txt}{space 9}terri_inco {c |}{col 21}{res}{space 2}-.2840445{col 33}{space 2} .6741466{col 44}{space 1}   -0.42{col 53}{space 3}0.674{col 61}{space 4}-1.605348{col 74}{space 3} 1.037259
{txt}{space 12}polity2 {c |}{col 21}{res}{space 2} -.015959{col 33}{space 2} .0658831{col 44}{space 1}   -0.24{col 53}{space 3}0.809{col 61}{space 4}-.1450875{col 74}{space 3} .1131694
{txt}{space 8}mediation_1 {c |}{col 21}{res}{space 2} .9055836{col 33}{space 2} .4468452{col 44}{space 1}    2.03{col 53}{space 3}0.043{col 61}{space 4}  .029783{col 74}{space 3} 1.781384
{txt}{space 9}ethnic_fra {c |}{col 21}{res}{space 2}-1.017989{col 33}{space 2} 1.158531{col 44}{space 1}   -0.88{col 53}{space 3}0.380{col 61}{space 4}-3.288669{col 74}{space 3}  1.25269
{txt}{space 13}ln_pop {c |}{col 21}{res}{space 2} .1363196{col 33}{space 2} .2069795{col 44}{space 1}    0.66{col 53}{space 3}0.510{col 61}{space 4}-.2693527{col 74}{space 3} .5419919
{txt}infantmort_rate1000 {c |}{col 21}{res}{space 2} .0133024{col 33}{space 2} .0071453{col 44}{space 1}    1.86{col 53}{space 3}0.063{col 61}{space 4}-.0007021{col 74}{space 3}  .027307
{txt}{space 8}ln_duration {c |}{col 21}{res}{space 2} .3051005{col 33}{space 2} .2260954{col 44}{space 1}    1.35{col 53}{space 3}0.177{col 61}{space 4}-.1380383{col 74}{space 3} .7482393
{txt}{space 10}major_war {c |}{col 21}{res}{space 2} .8493793{col 33}{space 2} .3687578{col 44}{space 1}    2.30{col 53}{space 3}0.021{col 61}{space 4} .1266272{col 74}{space 3} 1.572131
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-5.644286{col 33}{space 2} 4.176164{col 44}{space 1}   -1.35{col 53}{space 3}0.177{col 61}{space 4}-13.82942{col 74}{space 3} 2.540846
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. //Table 4 - Robustness Tests 
. 
. gen provisions_loc_gov_army_pko = total_prov
{txt}(20 missing values generated)

{com}. replace provisions_loc_gov_army_pko =  provisions_loc_gov_army_pko-shaloc if shaloc==1
{txt}(9 real changes made)

{com}. replace provisions_loc_gov_army_pko =  provisions_loc_gov_army_pko-intgov if intgov==1
{txt}(32 real changes made)

{com}. replace provisions_loc_gov_army_pko =  provisions_loc_gov_army_pko-intarmy if intarmy==1
{txt}(68 real changes made)

{com}. replace provisions_loc_gov_army_pko =  provisions_loc_gov_army_pko-pko if pko==1
{txt}(37 real changes made)

{com}. 
. logit dyvi05  provisions_loc_gov_army_pko  no_dyad pre_accord  terri_inco polity mediation_1 major_war  ethnic_fra  ln_pop infantmort_rate1000  ln_duration shaloc intgov pko intarmy , cl(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-131.23728}  
Iteration 1:{space 3}log pseudolikelihood = {res:-105.88697}  
Iteration 2:{space 3}log pseudolikelihood = {res:-105.35434}  
Iteration 3:{space 3}log pseudolikelihood = {res: -105.3526}  
Iteration 4:{space 3}log pseudolikelihood = {res: -105.3526}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}       195
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     49.21
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res} -105.3526{txt}{col 51}Pseudo R2{col 67}= {res}    0.1972

{txt}{ralign 93:(Std. Err. adjusted for {res:45} clusters in ccode)}
{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}                     dyvi05{col 29}{c |}      Coef.{col 41}   Std. Err.{col 53}      z{col 61}   P>|z|{col 69}     [95% Con{col 82}f. Interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
provisions_loc_gov_army_pko {c |}{col 29}{res}{space 2}-.2729423{col 41}{space 2} .1043809{col 52}{space 1}   -2.61{col 61}{space 3}0.009{col 69}{space 4} -.477525{col 82}{space 3}-.0683595
{txt}{space 20}no_dyad {c |}{col 29}{res}{space 2} .7734629{col 41}{space 2} .7328361{col 52}{space 1}    1.06{col 61}{space 3}0.291{col 69}{space 4}-.6628694{col 82}{space 3} 2.209795
{txt}{space 17}pre_accord {c |}{col 29}{res}{space 2}-1.129407{col 41}{space 2} .4830541{col 52}{space 1}   -2.34{col 61}{space 3}0.019{col 69}{space 4}-2.076176{col 82}{space 3}-.1826386
{txt}{space 17}terri_inco {c |}{col 29}{res}{space 2}-.1880751{col 41}{space 2} .7053743{col 52}{space 1}   -0.27{col 61}{space 3}0.790{col 69}{space 4}-1.570583{col 82}{space 3} 1.194433
{txt}{space 20}polity2 {c |}{col 29}{res}{space 2}-.0279248{col 41}{space 2} .0704604{col 52}{space 1}   -0.40{col 61}{space 3}0.692{col 69}{space 4}-.1660247{col 82}{space 3} .1101751
{txt}{space 16}mediation_1 {c |}{col 29}{res}{space 2} .8894259{col 41}{space 2} .4730055{col 52}{space 1}    1.88{col 61}{space 3}0.060{col 69}{space 4}-.0376478{col 82}{space 3}   1.8165
{txt}{space 18}major_war {c |}{col 29}{res}{space 2} 1.007541{col 41}{space 2} .3757576{col 52}{space 1}    2.68{col 61}{space 3}0.007{col 69}{space 4} .2710699{col 82}{space 3} 1.744013
{txt}{space 17}ethnic_fra {c |}{col 29}{res}{space 2}-1.015569{col 41}{space 2} 1.158946{col 52}{space 1}   -0.88{col 61}{space 3}0.381{col 69}{space 4}-3.287062{col 82}{space 3} 1.255923
{txt}{space 21}ln_pop {c |}{col 29}{res}{space 2} .2508225{col 41}{space 2} .2403728{col 52}{space 1}    1.04{col 61}{space 3}0.297{col 69}{space 4}-.2202995{col 82}{space 3} .7219445
{txt}{space 8}infantmort_rate1000 {c |}{col 29}{res}{space 2} .0163639{col 41}{space 2} .0073525{col 52}{space 1}    2.23{col 61}{space 3}0.026{col 69}{space 4} .0019533{col 82}{space 3} .0307744
{txt}{space 16}ln_duration {c |}{col 29}{res}{space 2} .2722307{col 41}{space 2} .2396689{col 52}{space 1}    1.14{col 61}{space 3}0.256{col 69}{space 4}-.1975117{col 82}{space 3} .7419731
{txt}{space 21}shaloc {c |}{col 29}{res}{space 2}-.4483871{col 41}{space 2} 1.076842{col 52}{space 1}   -0.42{col 61}{space 3}0.677{col 69}{space 4}-2.558959{col 82}{space 3} 1.662184
{txt}{space 21}intgov {c |}{col 29}{res}{space 2}-.4566179{col 41}{space 2} .5974329{col 52}{space 1}   -0.76{col 61}{space 3}0.445{col 69}{space 4}-1.627565{col 82}{space 3} .7143291
{txt}{space 24}pko {c |}{col 29}{res}{space 2} .5934644{col 41}{space 2}  .389713{col 52}{space 1}    1.52{col 61}{space 3}0.128{col 69}{space 4}-.1703591{col 82}{space 3} 1.357288
{txt}{space 20}intarmy {c |}{col 29}{res}{space 2}  -.33907{col 41}{space 2} .4411286{col 52}{space 1}   -0.77{col 61}{space 3}0.442{col 69}{space 4}-1.203666{col 82}{space 3} .5255261
{txt}{space 22}_cons {c |}{col 29}{res}{space 2}-7.123323{col 41}{space 2} 4.389186{col 52}{space 1}   -1.62{col 61}{space 3}0.105{col 69}{space 4}-15.72597{col 82}{space 3} 1.479323
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. gen provisionsPAM_loc_gov_army_pko = pam_ucdptotal
{txt}(20 missing values generated)

{com}. replace provisionsPAM_loc_gov_army_pko =  provisionsPAM_loc_gov_army_pko-shaloc if shaloc==1
{txt}(9 real changes made)

{com}. replace provisionsPAM_loc_gov_army_pko =  provisionsPAM_loc_gov_army_pko-intgov if intgov==1
{txt}(32 real changes made)

{com}. replace provisionsPAM_loc_gov_army_pko=  provisionsPAM_loc_gov_army_pko-intarmy if intarmy==1
{txt}(68 real changes made)

{com}. replace provisionsPAM_loc_gov_army_pko=  provisionsPAM_loc_gov_army_pko-pko if pko==1
{txt}(37 real changes made)

{com}. 
. logit   dyvi05 provisionsPAM_loc_gov_army_pko  no_dyad pre_accord  terri_inco polity mediation_1 major_war  ethnic_fra  ln_pop infantmort_rate1000  ln_duration shaloc intgov pko intarmy , cl(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-131.23728}  
Iteration 1:{space 3}log pseudolikelihood = {res:-106.33609}  
Iteration 2:{space 3}log pseudolikelihood = {res: -105.5413}  
Iteration 3:{space 3}log pseudolikelihood = {res:-105.53441}  
Iteration 4:{space 3}log pseudolikelihood = {res:-105.53441}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}       195
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     46.61
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-105.53441{txt}{col 51}Pseudo R2{col 67}= {res}    0.1959

{txt}{ralign 96:(Std. Err. adjusted for {res:45} clusters in ccode)}
{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}    Robust
{col 1}                        dyvi05{col 32}{c |}      Coef.{col 44}   Std. Err.{col 56}      z{col 64}   P>|z|{col 72}     [95% Con{col 85}f. Interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
provisionsPAM_loc_gov_army_pko {c |}{col 32}{res}{space 2}-.1176863{col 44}{space 2} .0546531{col 55}{space 1}   -2.15{col 64}{space 3}0.031{col 72}{space 4}-.2248044{col 85}{space 3}-.0105682
{txt}{space 23}no_dyad {c |}{col 32}{res}{space 2} .7921738{col 44}{space 2} .7590889{col 55}{space 1}    1.04{col 64}{space 3}0.297{col 72}{space 4}-.6956131{col 85}{space 3} 2.279961
{txt}{space 20}pre_accord {c |}{col 32}{res}{space 2}-1.179638{col 44}{space 2} .4924828{col 55}{space 1}   -2.40{col 64}{space 3}0.017{col 72}{space 4}-2.144887{col 85}{space 3}-.2143897
{txt}{space 20}terri_inco {c |}{col 32}{res}{space 2}-.3670646{col 44}{space 2} .6954524{col 55}{space 1}   -0.53{col 64}{space 3}0.598{col 72}{space 4}-1.730126{col 85}{space 3} .9959971
{txt}{space 23}polity2 {c |}{col 32}{res}{space 2}-.0276786{col 44}{space 2} .0683925{col 55}{space 1}   -0.40{col 64}{space 3}0.686{col 72}{space 4}-.1617254{col 85}{space 3} .1063683
{txt}{space 19}mediation_1 {c |}{col 32}{res}{space 2} .8843586{col 44}{space 2} .4362772{col 55}{space 1}    2.03{col 64}{space 3}0.043{col 72}{space 4} .0292711{col 85}{space 3} 1.739446
{txt}{space 21}major_war {c |}{col 32}{res}{space 2} .9025432{col 44}{space 2} .3616584{col 55}{space 1}    2.50{col 64}{space 3}0.013{col 72}{space 4} .1937057{col 85}{space 3} 1.611381
{txt}{space 20}ethnic_fra {c |}{col 32}{res}{space 2}-1.238517{col 44}{space 2} 1.181902{col 55}{space 1}   -1.05{col 64}{space 3}0.295{col 72}{space 4}-3.555002{col 85}{space 3} 1.077968
{txt}{space 24}ln_pop {c |}{col 32}{res}{space 2} .2736621{col 44}{space 2} .2462707{col 55}{space 1}    1.11{col 64}{space 3}0.266{col 72}{space 4}-.2090197{col 85}{space 3} .7563438
{txt}{space 11}infantmort_rate1000 {c |}{col 32}{res}{space 2} .0134563{col 44}{space 2}  .007395{col 55}{space 1}    1.82{col 64}{space 3}0.069{col 72}{space 4}-.0010378{col 85}{space 3} .0279503
{txt}{space 19}ln_duration {c |}{col 32}{res}{space 2} .2958259{col 44}{space 2} .2318328{col 55}{space 1}    1.28{col 64}{space 3}0.202{col 72}{space 4}-.1585581{col 85}{space 3} .7502099
{txt}{space 24}shaloc {c |}{col 32}{res}{space 2}-.7412066{col 44}{space 2} .9719371{col 55}{space 1}   -0.76{col 64}{space 3}0.446{col 72}{space 4}-2.646168{col 85}{space 3} 1.163755
{txt}{space 24}intgov {c |}{col 32}{res}{space 2}-.7510769{col 44}{space 2} .5739781{col 55}{space 1}   -1.31{col 64}{space 3}0.191{col 72}{space 4}-1.876053{col 85}{space 3} .3738995
{txt}{space 27}pko {c |}{col 32}{res}{space 2} .4844757{col 44}{space 2} .4170423{col 55}{space 1}    1.16{col 64}{space 3}0.245{col 72}{space 4}-.3329122{col 85}{space 3} 1.301864
{txt}{space 23}intarmy {c |}{col 32}{res}{space 2}-.5005667{col 44}{space 2} .3972448{col 55}{space 1}   -1.26{col 64}{space 3}0.208{col 72}{space 4}-1.279152{col 85}{space 3} .2780189
{txt}{space 25}_cons {c |}{col 32}{res}{space 2} -7.61234{col 44}{space 2} 4.428786{col 55}{space 1}   -1.72{col 64}{space 3}0.086{col 72}{space 4} -16.2926{col 85}{space 3} 1.067921
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. //Robustness Tests - excludes PAM agreement and replicate Model 4
. logit   dyvi05 total_prov no_dyad pre_accord  terri_inco polity mediation_1  ethnic_fra  ln_pop infantmort_rate1000  ln_duration major_war  if full!=1, cl(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-95.291674}  
Iteration 1:{space 3}log pseudolikelihood = {res:-76.418551}  
Iteration 2:{space 3}log pseudolikelihood = {res:-76.232454}  
Iteration 3:{space 3}log pseudolikelihood = {res:-76.231707}  
Iteration 4:{space 3}log pseudolikelihood = {res:-76.231707}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}       138
{txt}{col 51}Wald chi2({res}11{txt}){col 67}= {res}     18.56
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0695
{txt}Log pseudolikelihood = {res}-76.231707{txt}{col 51}Pseudo R2{col 67}= {res}    0.2000

{txt}{ralign 85:(Std. Err. adjusted for {res:35} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             dyvi05{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}total_prov {c |}{col 21}{res}{space 2}-.2252974{col 33}{space 2} .0910141{col 44}{space 1}   -2.48{col 53}{space 3}0.013{col 61}{space 4}-.4036817{col 74}{space 3} -.046913
{txt}{space 12}no_dyad {c |}{col 21}{res}{space 2}  1.57963{col 33}{space 2} .9904825{col 44}{space 1}    1.59{col 53}{space 3}0.111{col 61}{space 4}-.3616801{col 74}{space 3}  3.52094
{txt}{space 9}pre_accord {c |}{col 21}{res}{space 2}-1.320791{col 33}{space 2} .5514537{col 44}{space 1}   -2.40{col 53}{space 3}0.017{col 61}{space 4} -2.40162{col 74}{space 3}-.2399617
{txt}{space 9}terri_inco {c |}{col 21}{res}{space 2} .2243814{col 33}{space 2} .7318147{col 44}{space 1}    0.31{col 53}{space 3}0.759{col 61}{space 4}-1.209949{col 74}{space 3} 1.658712
{txt}{space 12}polity2 {c |}{col 21}{res}{space 2}-.0312031{col 33}{space 2} .0803869{col 44}{space 1}   -0.39{col 53}{space 3}0.698{col 61}{space 4}-.1887586{col 74}{space 3} .1263523
{txt}{space 8}mediation_1 {c |}{col 21}{res}{space 2} 1.137217{col 33}{space 2} .5253789{col 44}{space 1}    2.16{col 53}{space 3}0.030{col 61}{space 4} .1074929{col 74}{space 3}  2.16694
{txt}{space 9}ethnic_fra {c |}{col 21}{res}{space 2}-1.867068{col 33}{space 2} 1.296434{col 44}{space 1}   -1.44{col 53}{space 3}0.150{col 61}{space 4}-4.408031{col 74}{space 3} .6738952
{txt}{space 13}ln_pop {c |}{col 21}{res}{space 2} .4834935{col 33}{space 2} .2931374{col 44}{space 1}    1.65{col 53}{space 3}0.099{col 61}{space 4}-.0910453{col 74}{space 3} 1.058032
{txt}infantmort_rate1000 {c |}{col 21}{res}{space 2} .0195175{col 33}{space 2} .0105304{col 44}{space 1}    1.85{col 53}{space 3}0.064{col 61}{space 4}-.0011218{col 74}{space 3} .0401567
{txt}{space 8}ln_duration {c |}{col 21}{res}{space 2} .4440335{col 33}{space 2} .2772768{col 44}{space 1}    1.60{col 53}{space 3}0.109{col 61}{space 4}-.0994191{col 74}{space 3} .9874861
{txt}{space 10}major_war {c |}{col 21}{res}{space 2} .8909311{col 33}{space 2} .4871292{col 44}{space 1}    1.83{col 53}{space 3}0.067{col 61}{space 4}-.0638246{col 74}{space 3} 1.845687
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-12.90971{col 33}{space 2} 6.366528{col 44}{space 1}   -2.03{col 53}{space 3}0.043{col 61}{space 4}-25.38787{col 74}{space 3}-.4315435
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. //Robustness Tests - excludes process agreement and replicate Model 4 and Model 7
. logit   dyvi05 total_p no_dyad pre_accord  terri_inco polity mediation_1  ethnic_fra  ln_pop infantmort_rate1000  ln_duration major_war  if pa_type!=3 , cl(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-101.68004}  
Iteration 1:{space 3}log pseudolikelihood = {res:-83.710413}  
Iteration 2:{space 3}log pseudolikelihood = {res:-82.997611}  
Iteration 3:{space 3}log pseudolikelihood = {res: -82.99451}  
Iteration 4:{space 3}log pseudolikelihood = {res: -82.99451}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}       157
{txt}{col 51}Wald chi2({res}11{txt}){col 67}= {res}     24.45
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0109
{txt}Log pseudolikelihood = {res} -82.99451{txt}{col 51}Pseudo R2{col 67}= {res}    0.1838

{txt}{ralign 85:(Std. Err. adjusted for {res:42} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             dyvi05{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}total_prov {c |}{col 21}{res}{space 2}-.2181739{col 33}{space 2} .0674522{col 44}{space 1}   -3.23{col 53}{space 3}0.001{col 61}{space 4}-.3503777{col 74}{space 3}  -.08597
{txt}{space 12}no_dyad {c |}{col 21}{res}{space 2} .7006474{col 33}{space 2}  .678071{col 44}{space 1}    1.03{col 53}{space 3}0.301{col 61}{space 4}-.6283474{col 74}{space 3} 2.029642
{txt}{space 9}pre_accord {c |}{col 21}{res}{space 2}-1.443136{col 33}{space 2}  .705554{col 44}{space 1}   -2.05{col 53}{space 3}0.041{col 61}{space 4}-2.825996{col 74}{space 3}-.0602758
{txt}{space 9}terri_inco {c |}{col 21}{res}{space 2}-.8538322{col 33}{space 2} .7827846{col 44}{space 1}   -1.09{col 53}{space 3}0.275{col 61}{space 4}-2.388062{col 74}{space 3} .6803975
{txt}{space 12}polity2 {c |}{col 21}{res}{space 2}-.0364018{col 33}{space 2} .0729753{col 44}{space 1}   -0.50{col 53}{space 3}0.618{col 61}{space 4}-.1794308{col 74}{space 3} .1066271
{txt}{space 8}mediation_1 {c |}{col 21}{res}{space 2} 1.301221{col 33}{space 2} .5423575{col 44}{space 1}    2.40{col 53}{space 3}0.016{col 61}{space 4} .2382198{col 74}{space 3} 2.364222
{txt}{space 9}ethnic_fra {c |}{col 21}{res}{space 2}-.7835541{col 33}{space 2} 1.387037{col 44}{space 1}   -0.56{col 53}{space 3}0.572{col 61}{space 4}-3.502097{col 74}{space 3} 1.934989
{txt}{space 13}ln_pop {c |}{col 21}{res}{space 2} .2347958{col 33}{space 2} .2419775{col 44}{space 1}    0.97{col 53}{space 3}0.332{col 61}{space 4}-.2394714{col 74}{space 3}  .709063
{txt}infantmort_rate1000 {c |}{col 21}{res}{space 2} .0097713{col 33}{space 2} .0083519{col 44}{space 1}    1.17{col 53}{space 3}0.242{col 61}{space 4}-.0065981{col 74}{space 3} .0261407
{txt}{space 8}ln_duration {c |}{col 21}{res}{space 2} .2012426{col 33}{space 2} .2497845{col 44}{space 1}    0.81{col 53}{space 3}0.420{col 61}{space 4} -.288326{col 74}{space 3} .6908112
{txt}{space 10}major_war {c |}{col 21}{res}{space 2} .9973034{col 33}{space 2} .4543084{col 44}{space 1}    2.20{col 53}{space 3}0.028{col 61}{space 4} .1068752{col 74}{space 3} 1.887732
{txt}{space 14}_cons {c |}{col 21}{res}{space 2} -6.14221{col 33}{space 2} 4.328058{col 44}{space 1}   -1.42{col 53}{space 3}0.156{col 61}{space 4}-14.62505{col 74}{space 3} 2.340628
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit   dyvi05 pam_ucdptotal no_dyad pre_accord  terri_inco polity mediation_1  ethnic_fra  ln_pop infantmort_rate1000  ln_duration major_war  if pa_type!=3 , cl(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-101.68004}  
Iteration 1:{space 3}log pseudolikelihood = {res:-84.364627}  
Iteration 2:{space 3}log pseudolikelihood = {res:-83.455004}  
Iteration 3:{space 3}log pseudolikelihood = {res:-83.444828}  
Iteration 4:{space 3}log pseudolikelihood = {res:-83.444823}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}       157
{txt}{col 51}Wald chi2({res}11{txt}){col 67}= {res}     24.12
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0122
{txt}Log pseudolikelihood = {res}-83.444823{txt}{col 51}Pseudo R2{col 67}= {res}    0.1793

{txt}{ralign 85:(Std. Err. adjusted for {res:42} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             dyvi05{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}pam_ucdptotal {c |}{col 21}{res}{space 2}-.1109212{col 33}{space 2} .0391229{col 44}{space 1}   -2.84{col 53}{space 3}0.005{col 61}{space 4}-.1876007{col 74}{space 3}-.0342417
{txt}{space 12}no_dyad {c |}{col 21}{res}{space 2} .6882618{col 33}{space 2}  .705369{col 44}{space 1}    0.98{col 53}{space 3}0.329{col 61}{space 4}-.6942361{col 74}{space 3}  2.07076
{txt}{space 9}pre_accord {c |}{col 21}{res}{space 2}-1.501316{col 33}{space 2} .7092687{col 44}{space 1}   -2.12{col 53}{space 3}0.034{col 61}{space 4}-2.891457{col 74}{space 3}-.1111752
{txt}{space 9}terri_inco {c |}{col 21}{res}{space 2}-1.002636{col 33}{space 2} .8338067{col 44}{space 1}   -1.20{col 53}{space 3}0.229{col 61}{space 4}-2.636867{col 74}{space 3} .6315952
{txt}{space 12}polity2 {c |}{col 21}{res}{space 2}-.0364972{col 33}{space 2} .0716094{col 44}{space 1}   -0.51{col 53}{space 3}0.610{col 61}{space 4} -.176849{col 74}{space 3} .1038545
{txt}{space 8}mediation_1 {c |}{col 21}{res}{space 2} 1.315256{col 33}{space 2} .5209115{col 44}{space 1}    2.52{col 53}{space 3}0.012{col 61}{space 4} .2942886{col 74}{space 3} 2.336224
{txt}{space 9}ethnic_fra {c |}{col 21}{res}{space 2}-.9695779{col 33}{space 2} 1.418855{col 44}{space 1}   -0.68{col 53}{space 3}0.494{col 61}{space 4}-3.750483{col 74}{space 3} 1.811327
{txt}{space 13}ln_pop {c |}{col 21}{res}{space 2} .2204278{col 33}{space 2} .2529651{col 44}{space 1}    0.87{col 53}{space 3}0.384{col 61}{space 4}-.2753747{col 74}{space 3} .7162303
{txt}infantmort_rate1000 {c |}{col 21}{res}{space 2} .0062953{col 33}{space 2} .0085713{col 44}{space 1}    0.73{col 53}{space 3}0.463{col 61}{space 4}-.0105041{col 74}{space 3} .0230946
{txt}{space 8}ln_duration {c |}{col 21}{res}{space 2} .2331051{col 33}{space 2}  .260659{col 44}{space 1}    0.89{col 53}{space 3}0.371{col 61}{space 4}-.2777773{col 74}{space 3} .7439874
{txt}{space 10}major_war {c |}{col 21}{res}{space 2} .7880193{col 33}{space 2}  .430053{col 44}{space 1}    1.83{col 53}{space 3}0.067{col 61}{space 4}-.0548691{col 74}{space 3} 1.630908
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-5.983528{col 33}{space 2} 4.649391{col 44}{space 1}   -1.29{col 53}{space 3}0.198{col 61}{space 4}-15.09617{col 74}{space 3}  3.12911
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. 
. //Table 5 Marginal Effects
. //Model 4 and 7  in Table 2 for negotiation in Future
. logit   pa_lead_pa total_prov no_dyad pre_accord process terri_inco polity mediation_1  ethnic_fra  ln_pop infantmort_rate1000  ln_duration major_war, cl(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-111.30276}  
Iteration 1:{space 3}log pseudolikelihood = {res:-86.623959}  
Iteration 2:{space 3}log pseudolikelihood = {res:-84.217151}  
Iteration 3:{space 3}log pseudolikelihood = {res: -84.12162}  
Iteration 4:{space 3}log pseudolikelihood = {res:-84.121235}  
Iteration 5:{space 3}log pseudolikelihood = {res:-84.121235}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}       196
{txt}{col 51}Wald chi2({res}12{txt}){col 67}= {res}     42.47
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-84.121235{txt}{col 51}Pseudo R2{col 67}= {res}    0.2442

{txt}{ralign 85:(Std. Err. adjusted for {res:45} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}         pa_lead_pa{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}total_prov {c |}{col 21}{res}{space 2}-.1365027{col 33}{space 2} .0692292{col 44}{space 1}   -1.97{col 53}{space 3}0.049{col 61}{space 4}-.2721894{col 74}{space 3}-.0008161
{txt}{space 12}no_dyad {c |}{col 21}{res}{space 2} .7728064{col 33}{space 2} .5457762{col 44}{space 1}    1.42{col 53}{space 3}0.157{col 61}{space 4}-.2968952{col 74}{space 3} 1.842508
{txt}{space 9}pre_accord {c |}{col 21}{res}{space 2} .3955801{col 33}{space 2} .3786725{col 44}{space 1}    1.04{col 53}{space 3}0.296{col 61}{space 4}-.3466043{col 74}{space 3} 1.137764
{txt}{space 6}process_agree {c |}{col 21}{res}{space 2} 1.900951{col 33}{space 2} .6381919{col 44}{space 1}    2.98{col 53}{space 3}0.003{col 61}{space 4} .6501178{col 74}{space 3} 3.151784
{txt}{space 9}terri_inco {c |}{col 21}{res}{space 2} .0945582{col 33}{space 2} .6768353{col 44}{space 1}    0.14{col 53}{space 3}0.889{col 61}{space 4}-1.232015{col 74}{space 3} 1.421131
{txt}{space 12}polity2 {c |}{col 21}{res}{space 2}-.0495887{col 33}{space 2} .0448404{col 44}{space 1}   -1.11{col 53}{space 3}0.269{col 61}{space 4}-.1374742{col 74}{space 3} .0382969
{txt}{space 8}mediation_1 {c |}{col 21}{res}{space 2} -.009315{col 33}{space 2}  .453911{col 44}{space 1}   -0.02{col 53}{space 3}0.984{col 61}{space 4}-.8989642{col 74}{space 3} .8803343
{txt}{space 9}ethnic_fra {c |}{col 21}{res}{space 2} .5744621{col 33}{space 2} .8555199{col 44}{space 1}    0.67{col 53}{space 3}0.502{col 61}{space 4}-1.102326{col 74}{space 3}  2.25125
{txt}{space 13}ln_pop {c |}{col 21}{res}{space 2}-.0938978{col 33}{space 2}  .125154{col 44}{space 1}   -0.75{col 53}{space 3}0.453{col 61}{space 4}-.3391952{col 74}{space 3} .1513996
{txt}infantmort_rate1000 {c |}{col 21}{res}{space 2} .0183075{col 33}{space 2} .0059952{col 44}{space 1}    3.05{col 53}{space 3}0.002{col 61}{space 4}  .006557{col 74}{space 3} .0300579
{txt}{space 8}ln_duration {c |}{col 21}{res}{space 2} .4903867{col 33}{space 2} .1923933{col 44}{space 1}    2.55{col 53}{space 3}0.011{col 61}{space 4} .1133027{col 74}{space 3} .8674707
{txt}{space 10}major_war {c |}{col 21}{res}{space 2} .0288579{col 33}{space 2} .4013579{col 44}{space 1}    0.07{col 53}{space 3}0.943{col 61}{space 4}-.7577891{col 74}{space 3} .8155048
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-3.508887{col 33}{space 2} 1.819912{col 44}{space 1}   -1.93{col 53}{space 3}0.054{col 61}{space 4}-7.075848{col 74}{space 3} .0580747
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. mfx compute, at (mean, total_prov=0)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .90949144
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0112365      .00227   -4.95   0.000  -.015686 -.006787         0
 {txt}no_dyad {c |}  {res} .0636149       .0489    1.30   0.193  -.032228  .159458   1.14796
{txt}pre_ac~d*{c |}  {res} .0353304      .03861    0.91   0.360  -.040351  .111012   .744898
{txt}proces~e*{c |}  {res} .1174416      .04748    2.47   0.013   .024376  .210507   .265306
{txt}terri_~o*{c |}  {res} .0076084      .05252    0.14   0.885  -.095333   .11055   .204082
 {txt}polity2 {c |}  {res} -.004082      .00335   -1.22   0.223  -.010641  .002477   .285714
{txt}mediat~1*{c |}  {res}-.0007658      .03732   -0.02   0.984   -.07392  .072388   .663265
{txt}ethnic~a {c |}  {res} .0472879      .07122    0.66   0.507  -.092296  .186872   .617573
  {txt}ln_pop {c |}  {res}-.0077294       .0112   -0.69   0.490   -.02968  .014222   16.1947
{txt}inf~1000 {c |}  {res}  .001507      .00064    2.34   0.019   .000243  .002771   80.7332
{txt}ln_dur~n {c |}  {res}  .040367       .0228    1.77   0.077  -.004322  .085056   7.75547
{txt}major_~r*{c |}  {res} .0023784      .03307    0.07   0.943  -.062446  .067202    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, total_prov=21)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .36375194
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0315917      .00781   -4.05   0.000  -.046896 -.016287        21
 {txt}no_dyad {c |}  {res} .1788556      .13849    1.29   0.197  -.092585  .450296   1.14796
{txt}pre_ac~d*{c |}  {res} .0887723      .08476    1.05   0.295  -.077348  .254892   .744898
{txt}proces~e*{c |}  {res} .4412879      .12907    3.42   0.001   .188313  .694263   .265306
{txt}terri_~o*{c |}  {res} .0220444      .15832    0.14   0.889  -.288259  .332348   .204082
 {txt}polity2 {c |}  {res}-.0114766       .0099   -1.16   0.246  -.030884  .007931   .285714
{txt}mediat~1*{c |}  {res}-.0021567      .10523   -0.02   0.984  -.208408  .204095   .663265
{txt}ethnic~a {c |}  {res} .1329515       .1975    0.67   0.501  -.254133  .520035   .617573
  {txt}ln_pop {c |}  {res}-.0217314      .03087   -0.70   0.481  -.082237  .038774   16.1947
{txt}inf~1000 {c |}  {res}  .004237      .00164    2.58   0.010   .001021  .007453   80.7332
{txt}ln_dur~n {c |}  {res} .1134934      .05866    1.93   0.053  -.001486  .228473   7.75547
{txt}major_~r*{c |}  {res}  .006676      .09261    0.07   0.943   -.17484  .188192    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean, no_dyad=1)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .80578199
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0213623      .01001   -2.13   0.033   -.04099 -.001734   5.64286
 {txt}no_dyad {c |}  {res} .1209422      .08724    1.39   0.166  -.050036   .29192         1
{txt}pre_ac~d*{c |}  {res} .0655892      .06649    0.99   0.324  -.064726  .195905   .744898
{txt}proces~e*{c |}  {res} .2289818      .05182    4.42   0.000   .127424   .33054   .265306
{txt}terri_~o*{c |}  {res} .0145457      .10186    0.14   0.886  -.185097  .214188   .204082
 {txt}polity2 {c |}  {res}-.0077605      .00678   -1.14   0.253  -.021054  .005533   .285714
{txt}mediat~1*{c |}  {res}-.0014564      .07094   -0.02   0.984  -.140496  .137584   .663265
{txt}ethnic~a {c |}  {res} .0899018      .13389    0.67   0.502  -.172518  .352322   .617573
  {txt}ln_pop {c |}  {res}-.0146948      .01978   -0.74   0.457  -.053456  .024067   16.1947
{txt}inf~1000 {c |}  {res} .0028651      .00082    3.47   0.001   .001249  .004481   80.7332
{txt}ln_dur~n {c |}  {res} .0767442      .02948    2.60   0.009   .018959  .134529   7.75547
{txt}major_~r*{c |}  {res} .0045203      .06291    0.07   0.943  -.118787  .127827    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, no_dyad=3)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .95112803
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0063451      .00686   -0.93   0.355  -.019783  .007093   5.64286
 {txt}no_dyad {c |}  {res} .0359227      .01186    3.03   0.002    .01267  .059176         3
{txt}pre_ac~d*{c |}  {res} .0201495      .02928    0.69   0.491  -.037244  .077543   .744898
{txt}proces~e*{c |}  {res} .0658585      .06553    1.01   0.315  -.062576  .194293   .265306
{txt}terri_~o*{c |}  {res} .0042868      .02953    0.15   0.885  -.053594  .062167   .204082
 {txt}polity2 {c |}  {res}-.0023051      .00265   -0.87   0.385  -.007507  .002896   .285714
{txt}mediat~1*{c |}  {res}-.0004324      .02094   -0.02   0.984  -.041472  .040607   .663265
{txt}ethnic~a {c |}  {res}  .026703      .04713    0.57   0.571  -.065677  .119083   .617573
  {txt}ln_pop {c |}  {res}-.0043647      .00612   -0.71   0.476   -.01636   .00763   16.1947
{txt}inf~1000 {c |}  {res}  .000851      .00089    0.96   0.337  -.000888   .00259   80.7332
{txt}ln_dur~n {c |}  {res} .0227949      .02191    1.04   0.298   -.02015   .06574   7.75547
{txt}major_~r*{c |}  {res} .0013432      .01898    0.07   0.944  -.035857  .038543    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean, pre_accord=0)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .77600042
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0237274      .01205   -1.97   0.049  -.047342 -.000113   5.64286
 {txt}no_dyad {c |}  {res} .1343321      .09236    1.45   0.146  -.046696   .31536   1.14796
{txt}pre_ac~d*{c |}  {res} .0612743      .06256    0.98   0.327  -.061337  .183886         0
{txt}proces~e*{c |}  {res}  .256741      .07255    3.54   0.000   .114543  .398939   .265306
{txt}terri_~o*{c |}  {res} .0161822      .11401    0.14   0.887  -.207281  .239645   .204082
 {txt}polity2 {c |}  {res}-.0086197      .00795   -1.08   0.278  -.024207  .006968   .285714
{txt}mediat~1*{c |}  {res}-.0016178      .07883   -0.02   0.984  -.156119  .152884   .663265
{txt}ethnic~a {c |}  {res} .0998552      .14919    0.67   0.503  -.192558  .392268   .617573
  {txt}ln_pop {c |}  {res}-.0163217      .02124   -0.77   0.442  -.057955  .025312   16.1947
{txt}inf~1000 {c |}  {res} .0031823      .00113    2.83   0.005   .000975  .005389   80.7332
{txt}ln_dur~n {c |}  {res} .0852409      .03323    2.57   0.010   .020116  .150366   7.75547
{txt}major_~r*{c |}  {res} .0050202      .06976    0.07   0.943  -.131698  .141738    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, pre_accord=1)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .83727474
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0185979      .00872   -2.13   0.033  -.035699 -.001497   5.64286
 {txt}no_dyad {c |}  {res} .1052916      .07119    1.48   0.139   -.03423  .244813   1.14796
{txt}pre_ac~d*{c |}  {res} .0612743      .06256    0.98   0.327  -.061337  .183886         1
{txt}proces~e*{c |}  {res} .1975864      .04744    4.17   0.000   .104615  .290558   .265306
{txt}terri_~o*{c |}  {res} .0126419       .0881    0.14   0.886  -.160024  .185307   .204082
 {txt}polity2 {c |}  {res}-.0067562      .00571   -1.18   0.237  -.017948  .004436   .285714
{txt}mediat~1*{c |}  {res}-.0012678      .06172   -0.02   0.984  -.122231  .119695   .663265
{txt}ethnic~a {c |}  {res}  .078268      .11676    0.67   0.503  -.150581  .307117   .617573
  {txt}ln_pop {c |}  {res}-.0127932      .01726   -0.74   0.459  -.046618  .021031   16.1947
{txt}inf~1000 {c |}  {res} .0024943      .00073    3.41   0.001   .001061  .003927   80.7332
{txt}ln_dur~n {c |}  {res} .0668131      .02589    2.58   0.010   .016076   .11755   7.75547
{txt}major_~r*{c |}  {res} .0039357      .05487    0.07   0.943  -.103609  .111481    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean, process=0)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .73746646
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0264283      .01313   -2.01   0.044  -.052155 -.000702   5.64286
 {txt}no_dyad {c |}  {res} .1496228      .10224    1.46   0.143  -.050767  .350012   1.14796
{txt}pre_ac~d*{c |}  {res} .0799346       .0797    1.00   0.316  -.076273  .236143   .744898
{txt}proces~e*{c |}  {res} .2120254      .04839    4.38   0.000   .117181  .306869         0
{txt}terri_~o*{c |}  {res}  .018062      .12741    0.14   0.887  -.231655  .267779   .204082
 {txt}polity2 {c |}  {res}-.0096008      .00848   -1.13   0.258  -.026222  .007021   .285714
{txt}mediat~1*{c |}  {res}-.0018022      .08779   -0.02   0.984  -.173862  .170258   .663265
{txt}ethnic~a {c |}  {res} .1112214       .1671    0.67   0.506  -.216287   .43873   .617573
  {txt}ln_pop {c |}  {res}-.0181795      .02405   -0.76   0.450  -.065318  .028958   16.1947
{txt}inf~1000 {c |}  {res} .0035445      .00109    3.26   0.001   .001412  .005677   80.7332
{txt}ln_dur~n {c |}  {res} .0949436      .03442    2.76   0.006    .02748  .162408   7.75547
{txt}major_~r*{c |}  {res}  .005591      .07784    0.07   0.943   -.14698  .158162    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, process=1)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .94949187
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0065463      .00393   -1.66   0.096  -.014258  .001166   5.64286
 {txt}no_dyad {c |}  {res} .0370615      .03001    1.23   0.217   -.02176  .095883   1.14796
{txt}pre_ac~d*{c |}  {res}   .02078      .02435    0.85   0.393  -.026946  .068506   .744898
{txt}proces~e*{c |}  {res} .2120254      .04839    4.38   0.000   .117181  .306869         1
{txt}terri_~o*{c |}  {res} .0044231      .03019    0.15   0.884  -.054743  .063589   .204082
 {txt}polity2 {c |}  {res}-.0023781      .00221   -1.07   0.283  -.006717  .001961   .285714
{txt}mediat~1*{c |}  {res}-.0004461      .02169   -0.02   0.984  -.042965  .042073   .663265
{txt}ethnic~a {c |}  {res} .0275495      .04132    0.67   0.505   -.05344  .108539   .617573
  {txt}ln_pop {c |}  {res}-.0045031      .00658   -0.68   0.494  -.017395  .008389   16.1947
{txt}inf~1000 {c |}  {res}  .000878      .00049    1.79   0.073  -.000081  .001837   80.7332
{txt}ln_dur~n {c |}  {res} .0235175      .01562    1.51   0.132  -.007102  .054137   7.75547
{txt}major_~r*{c |}  {res} .0013858      .01932    0.07   0.943  -.036482  .039254    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean, terri_inco=0)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .82022581
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0201281      .00979   -2.06   0.040  -.039323 -.000933   5.64286
 {txt}no_dyad {c |}  {res} .1139545      .07785    1.46   0.143  -.038626  .266535   1.14796
{txt}pre_ac~d*{c |}  {res} .0620002      .06431    0.96   0.335   -.06405   .18805   .744898
{txt}proces~e*{c |}  {res} .2148452      .05557    3.87   0.000   .105931  .323759   .265306
{txt}terri_~o*{c |}  {res} .0135236      .09451    0.14   0.886  -.171715  .198763         0
 {txt}polity2 {c |}  {res}-.0073121       .0066   -1.11   0.268  -.020247  .005622   .285714
{txt}mediat~1*{c |}  {res}-.0013722      .06688   -0.02   0.984  -.132457  .129713   .663265
{txt}ethnic~a {c |}  {res} .0847076      .12992    0.65   0.514  -.169937  .339352   .617573
  {txt}ln_pop {c |}  {res}-.0138457      .01878   -0.74   0.461  -.050656  .022965   16.1947
{txt}inf~1000 {c |}  {res} .0026995      .00089    3.03   0.002   .000952  .004447   80.7332
{txt}ln_dur~n {c |}  {res} .0723102      .02936    2.46   0.014   .014771  .129849   7.75547
{txt}major_~r*{c |}  {res} .0042593      .05935    0.07   0.943  -.112073  .120592    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, terri_inco=1)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .83374937
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0189208      .01067   -1.77   0.076  -.039824  .001982   5.64286
 {txt}no_dyad {c |}  {res} .1071198      .07942    1.35   0.177  -.048546  .262786   1.14796
{txt}pre_ac~d*{c |}  {res} .0584599      .05994    0.98   0.329  -.059026  .175946   .744898
{txt}proces~e*{c |}  {res} .2012063      .08373    2.40   0.016   .037092   .36532   .265306
{txt}terri_~o*{c |}  {res} .0135236      .09451    0.14   0.886  -.171715  .198763         1
 {txt}polity2 {c |}  {res}-.0068736      .00541   -1.27   0.204  -.017468  .003721   .285714
{txt}mediat~1*{c |}  {res}-.0012899      .06256   -0.02   0.984  -.123903  .121323   .663265
{txt}ethnic~a {c |}  {res}  .079627      .10774    0.74   0.460  -.131533  .290787   .617573
  {txt}ln_pop {c |}  {res}-.0130153      .01677   -0.78   0.438  -.045882  .019851   16.1947
{txt}inf~1000 {c |}  {res} .0025376      .00102    2.50   0.013   .000546  .004529   80.7332
{txt}ln_dur~n {c |}  {res} .0679732      .03065    2.22   0.027   .007896   .12805   7.75547
{txt}major_~r*{c |}  {res}  .004004      .05567    0.07   0.943  -.105106  .113114    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean, mediation_1=0)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .82395177
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0198004      .01061   -1.87   0.062  -.040588  .000987   5.64286
 {txt}no_dyad {c |}  {res} .1120996      .07137    1.57   0.116  -.027782  .251982   1.14796
{txt}pre_ac~d*{c |}  {res} .0610422      .06124    1.00   0.319  -.058989  .181074   .744898
{txt}proces~e*{c |}  {res} .2111265      .05847    3.61   0.000   .096519  .325734   .265306
{txt}terri_~o*{c |}  {res}  .013469      .09272    0.15   0.885  -.168261  .195199   .204082
 {txt}polity2 {c |}  {res}-.0071931      .00583   -1.23   0.218  -.018626   .00424   .285714
{txt}mediat~1*{c |}  {res}-.0013553      .06599   -0.02   0.984  -.130696  .127986         0
{txt}ethnic~a {c |}  {res} .0833287      .11938    0.70   0.485  -.150652   .31731   .617573
  {txt}ln_pop {c |}  {res}-.0136204      .01731   -0.79   0.431  -.047546  .020306   16.1947
{txt}inf~1000 {c |}  {res} .0026556      .00084    3.15   0.002   .001002  .004309   80.7332
{txt}ln_dur~n {c |}  {res} .0711332      .02583    2.75   0.006     .0205  .121766   7.75547
{txt}major_~r*{c |}  {res}   .00419      .05859    0.07   0.943  -.110637  .119016    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, mediation_1=1)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .82259651
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}  -.01992      .00926   -2.15   0.031  -.038071 -.001769   5.64286
 {txt}no_dyad {c |}  {res} .1127768      .07999    1.41   0.159  -.044002  .269555   1.14796
{txt}pre_ac~d*{c |}  {res} .0613922      .06399    0.96   0.337   -.06403  .186815   .744898
{txt}proces~e*{c |}  {res} .2124825      .05716    3.72   0.000   .100456  .324509   .265306
{txt}terri_~o*{c |}  {res} .0135513      .09544    0.14   0.887  -.173513  .200615   .204082
 {txt}polity2 {c |}  {res}-.0072365      .00654   -1.11   0.268   -.02005  .005577   .285714
{txt}mediat~1*{c |}  {res}-.0013553      .06599   -0.02   0.984  -.130696  .127986         1
{txt}ethnic~a {c |}  {res} .0838321      .12796    0.66   0.512   -.16696  .334624   .617573
  {txt}ln_pop {c |}  {res}-.0137026      .01879   -0.73   0.466  -.050536   .02313   16.1947
{txt}inf~1000 {c |}  {res} .0026716      .00088    3.03   0.002   .000942  .004401   80.7332
{txt}ln_dur~n {c |}  {res} .0715629      .02982    2.40   0.016   .013118  .130008   7.75547
{txt}major_~r*{c |}  {res} .0042153       .0586    0.07   0.943  -.110635  .119065    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean,  ln_duration = 2.64)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .27460547
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res} -.027191      .01973   -1.38   0.168  -.065869  .011487   5.64286
 {txt}no_dyad {c |}  {res}  .153941      .11641    1.32   0.186  -.074222  .382104   1.14796
{txt}pre_ac~d*{c |}  {res} .0752225      .07969    0.94   0.345  -.080959  .231404   .744898
{txt}proces~e*{c |}  {res}   .41866      .17591    2.38   0.017   .073879  .763441   .265306
{txt}terri_~o*{c |}  {res} .0190704      .13608    0.14   0.889  -.247632  .285772   .204082
 {txt}polity2 {c |}  {res}-.0098779      .00992   -1.00   0.319  -.029322  .009566   .285714
{txt}mediat~1*{c |}  {res}-.0018568       .0903   -0.02   0.984  -.178848  .175135   .663265
{txt}ethnic~a {c |}  {res} .1144313      .17209    0.66   0.506  -.222851  .451714   .617573
  {txt}ln_pop {c |}  {res}-.0187042      .02133   -0.88   0.381  -.060507  .023098   16.1947
{txt}inf~1000 {c |}  {res} .0036468      .00153    2.39   0.017   .000658  .006636   80.7332
{txt}ln_dur~n {c |}  {res} .0976837      .00944   10.35   0.000   .079183  .116185      2.64
{txt}major_~r*{c |}  {res} .0057446      .08014    0.07   0.943  -.151331  .162821    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean,  ln_duration = 9.81)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .92721613
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0092121      .00588   -1.57   0.117  -.020742  .002318   5.64286
 {txt}no_dyad {c |}  {res} .0521539       .0361    1.44   0.149  -.018598  .122906   1.14796
{txt}pre_ac~d*{c |}  {res} .0290869      .03344    0.87   0.384  -.036458  .094632   .744898
{txt}proces~e*{c |}  {res} .0959775      .04658    2.06   0.039   .004673  .187282   .265306
{txt}terri_~o*{c |}  {res} .0062317      .04258    0.15   0.884  -.077219  .089683   .204082
 {txt}polity2 {c |}  {res}-.0033466      .00307   -1.09   0.275  -.009355  .002662   .285714
{txt}mediat~1*{c |}  {res}-.0006278      .03051   -0.02   0.984  -.060417  .059162   .663265
{txt}ethnic~a {c |}  {res} .0387684      .05833    0.66   0.506  -.075547  .153084   .617573
  {txt}ln_pop {c |}  {res}-.0063368      .00755   -0.84   0.401  -.021134   .00846   16.1947
{txt}inf~1000 {c |}  {res} .0012355      .00045    2.73   0.006   .000347  .002124   80.7332
{txt}ln_dur~n {c |}  {res} .0330944      .00591    5.60   0.000   .021509  .044679      9.81
{txt}major_~r*{c |}  {res}   .00195      .02726    0.07   0.943  -.051474  .055374    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean,  major_war = 0)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .82072607
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0200843      .01019   -1.97   0.049  -.040055 -.000114   5.64286
 {txt}no_dyad {c |}  {res} .1137067      .07522    1.51   0.131  -.033729  .261142   1.14796
{txt}pre_ac~d*{c |}  {res} .0618723      .06207    1.00   0.319  -.059787  .183532   .744898
{txt}proces~e*{c |}  {res} .2143476      .05963    3.59   0.000   .097479  .331216   .265306
{txt}terri_~o*{c |}  {res} .0136644       .0957    0.14   0.886  -.173909  .201237   .204082
 {txt}polity2 {c |}  {res}-.0072962      .00583   -1.25   0.211  -.018725  .004133   .285714
{txt}mediat~1*{c |}  {res}-.0013692      .06662   -0.02   0.984  -.131939    .1292   .663265
{txt}ethnic~a {c |}  {res} .0845234      .12741    0.66   0.507  -.165195  .334241   .617573
  {txt}ln_pop {c |}  {res}-.0138156      .01854   -0.75   0.456  -.050147  .022516   16.1947
{txt}inf~1000 {c |}  {res} .0026937      .00088    3.08   0.002   .000977   .00441   80.7332
{txt}ln_dur~n {c |}  {res} .0721529       .0279    2.59   0.010   .017462  .126843   7.75547
{txt}major_~r*{c |}  {res} .0042068      .05859    0.07   0.943   -.11063  .119044         0
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean,  major_war = 1)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .82493284
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0197135      .00926   -2.13   0.033  -.037865 -.001562   5.64286
 {txt}no_dyad {c |}  {res} .1116077      .07831    1.43   0.154  -.041887  .265102   1.14796
{txt}pre_ac~d*{c |}  {res} .0607878      .06374    0.95   0.340  -.064132  .185707   .744898
{txt}proces~e*{c |}  {res} .2101424      .05378    3.91   0.000   .104732  .315553   .265306
{txt}terri_~o*{c |}  {res} .0134091      .09357    0.14   0.886  -.169994  .196812   .204082
 {txt}polity2 {c |}  {res}-.0071615      .00664   -1.08   0.281  -.020184  .005861   .285714
{txt}mediat~1*{c |}  {res}-.0013439      .06548   -0.02   0.984  -.129687  .126999   .663265
{txt}ethnic~a {c |}  {res}  .082963      .12312    0.67   0.500  -.158354  .324281   .617573
  {txt}ln_pop {c |}  {res}-.0135606      .01809   -0.75   0.454  -.049018  .021897   16.1947
{txt}inf~1000 {c |}  {res} .0026439      .00085    3.13   0.002   .000988    .0043   80.7332
{txt}ln_dur~n {c |}  {res}  .070821       .0286    2.48   0.013    .01476  .126882   7.75547
{txt}major_~r*{c |}  {res} .0042068      .05859    0.07   0.943   -.11063  .119044         1
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean, polity=-9)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .88054927
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0143577      .00708   -2.03   0.043   -.02823 -.000485   5.64286
 {txt}no_dyad {c |}  {res} .0812855      .05689    1.43   0.153  -.030225  .192796   1.14796
{txt}pre_ac~d*{c |}  {res} .0448398      .04571    0.98   0.327   -.04475  .134429   .744898
{txt}proces~e*{c |}  {res} .1509504      .06335    2.38   0.017   .026784  .275117   .265306
{txt}terri_~o*{c |}  {res} .0097369      .06651    0.15   0.884  -.120612  .140085   .204082
 {txt}polity2 {c |}  {res}-.0052158      .00286   -1.82   0.068  -.010824  .000392        -9
{txt}mediat~1*{c |}  {res}-.0009786      .04754   -0.02   0.984   -.09416  .092203   .663265
{txt}ethnic~a {c |}  {res} .0604232      .09338    0.65   0.518  -.122607  .243453   .617573
  {txt}ln_pop {c |}  {res}-.0098764      .01411   -0.70   0.484  -.037525  .017772   16.1947
{txt}inf~1000 {c |}  {res} .0019256      .00083    2.32   0.020   .000301   .00355   80.7332
{txt}ln_dur~n {c |}  {res}   .05158      .02653    1.94   0.052  -.000409  .103569   7.75547
{txt}major_~r*{c |}  {res} .0030388      .04282    0.07   0.943  -.080896  .086974    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, polity=10)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .74182146
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0261433      .01502   -1.74   0.082   -.05559  .003303   5.64286
 {txt}no_dyad {c |}  {res} .1480097      .10988    1.35   0.178  -.067346  .363365   1.14796
{txt}pre_ac~d*{c |}  {res} .0791473      .08389    0.94   0.345   -.08527  .243565   .744898
{txt}proces~e*{c |}  {res} .2863168       .0933    3.07   0.002    .10346  .469173   .265306
{txt}terri_~o*{c |}  {res}  .017863      .12725    0.14   0.888  -.231547  .267273   .204082
 {txt}polity2 {c |}  {res}-.0094973      .01026   -0.93   0.355  -.029611  .010616        10
{txt}mediat~1*{c |}  {res}-.0017827      .08695   -0.02   0.984    -.1722  .168634   .663265
{txt}ethnic~a {c |}  {res} .1100224      .16405    0.67   0.502  -.211509  .431554   .617573
  {txt}ln_pop {c |}  {res}-.0179835      .02355   -0.76   0.445  -.064137  .028169   16.1947
{txt}inf~1000 {c |}  {res} .0035063      .00133    2.63   0.008   .000897  .006116   80.7332
{txt}ln_dur~n {c |}  {res}   .09392      .03977    2.36   0.018    .01598   .17186   7.75547
{txt}major_~r*{c |}  {res} .0055308      .07643    0.07   0.942  -.144269  .155331    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean, ethnic_fra = 0.25)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .79018132
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0226314      .01184   -1.91   0.056  -.045838  .000576   5.64286
 {txt}no_dyad {c |}  {res} .1281273      .08982    1.43   0.154  -.047917  .304172   1.14796
{txt}pre_ac~d*{c |}  {res} .0692449      .07081    0.98   0.328  -.069544  .208034   .744898
{txt}proces~e*{c |}  {res} .2437553      .07986    3.05   0.002   .087237  .400273   .265306
{txt}terri_~o*{c |}  {res} .0154229      .10952    0.14   0.888  -.199238  .230083   .204082
 {txt}polity2 {c |}  {res}-.0082215      .00722   -1.14   0.255  -.022368  .005925   .285714
{txt}mediat~1*{c |}  {res} -.001543      .07524   -0.02   0.984  -.149002  .145916   .663265
{txt}ethnic~a {c |}  {res} .0952428      .15909    0.60   0.549  -.216577  .407062       .25
  {txt}ln_pop {c |}  {res}-.0155678      .02203   -0.71   0.480  -.058753  .027617   16.1947
{txt}inf~1000 {c |}  {res} .0030353      .00098    3.11   0.002   .001122  .004949   80.7332
{txt}ln_dur~n {c |}  {res} .0813036      .03519    2.31   0.021   .012337  .150271   7.75547
{txt}major_~r*{c |}  {res} .0047886      .06676    0.07   0.943  -.126055  .135632    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, ethnic_fra = 0.93) 

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .84769721
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0176234      .00857   -2.06   0.040  -.034416 -.000831   5.64286
 {txt}no_dyad {c |}  {res} .0997745      .06912    1.44   0.149  -.035694  .235243   1.14796
{txt}pre_ac~d*{c |}  {res} .0546242       .0572    0.95   0.340  -.057485  .166734   .744898
{txt}proces~e*{c |}  {res} .1867278      .05108    3.66   0.000   .086612  .286844   .265306
{txt}terri_~o*{c |}  {res} .0119727       .0824    0.15   0.884  -.149532  .173477   .204082
 {txt}polity2 {c |}  {res}-.0064022      .00568   -1.13   0.260  -.017534  .004729   .285714
{txt}mediat~1*{c |}  {res}-.0012014      .05842   -0.02   0.984  -.115698  .113295   .663265
{txt}ethnic~a {c |}  {res} .0741669      .09678    0.77   0.443  -.115517   .26385       .93
  {txt}ln_pop {c |}  {res}-.0121228      .01536   -0.79   0.430  -.042236   .01799   16.1947
{txt}inf~1000 {c |}  {res} .0023636      .00088    2.67   0.007   .000632  .004096   80.7332
{txt}ln_dur~n {c |}  {res} .0633122      .02546    2.49   0.013   .013419  .113206   7.75547
{txt}major_~r*{c |}  {res} .0037296       .0519    0.07   0.943  -.097996  .105456    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean, ln_pop = 13.21)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res}  .8602574
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0164096      .00974   -1.68   0.092  -.035507  .002688   5.64286
 {txt}no_dyad {c |}  {res} .0929026      .05917    1.57   0.116  -.023065   .20887   1.14796
{txt}pre_ac~d*{c |}  {res} .0510086      .05738    0.89   0.374  -.061458  .163475   .744898
{txt}proces~e*{c |}  {res} .1733288      .06722    2.58   0.010   .041575  .305082   .265306
{txt}terri_~o*{c |}  {res} .0111406      .07685    0.14   0.885  -.139488  .161769   .204082
 {txt}polity2 {c |}  {res}-.0059613      .00562   -1.06   0.289   -.01697  .005047   .285714
{txt}mediat~1*{c |}  {res}-.0011186      .05435   -0.02   0.984  -.107646  .105409   .663265
{txt}ethnic~a {c |}  {res} .0690587      .09684    0.71   0.476  -.120744  .258862   .617573
  {txt}ln_pop {c |}  {res}-.0112879      .01203   -0.94   0.348  -.034857  .012281     13.21
{txt}inf~1000 {c |}  {res} .0022008      .00092    2.40   0.017   .000401  .004001   80.7332
{txt}ln_dur~n {c |}  {res} .0589516      .01856    3.18   0.001   .022584  .095319   7.75547
{txt}major_~r*{c |}  {res} .0034729      .04837    0.07   0.943  -.091336  .098282    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, ln_pop = 20.63)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .75411996
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0253108      .01246   -2.03   0.042  -.049733 -.000889   5.64286
 {txt}no_dyad {c |}  {res} .1432961      .11638    1.23   0.218  -.084801  .371393   1.14796
{txt}pre_ac~d*{c |}  {res} .0768315      .07457    1.03   0.303  -.069329  .222992   .744898
{txt}proces~e*{c |}  {res} .2759541      .10074    2.74   0.006   .078507  .473402   .265306
{txt}terri_~o*{c |}  {res} .0172826      .12287    0.14   0.888  -.223537  .258102   .204082
 {txt}polity2 {c |}  {res}-.0091949        .008   -1.15   0.250  -.024867  .006477   .285714
{txt}mediat~1*{c |}  {res}-.0017259      .08423   -0.02   0.984  -.166815  .163364   .663265
{txt}ethnic~a {c |}  {res} .1065185      .17298    0.62   0.538  -.232511  .445548   .617573
  {txt}ln_pop {c |}  {res}-.0174108      .02806   -0.62   0.535  -.072409  .037588     20.63
{txt}inf~1000 {c |}  {res} .0033946      .00133    2.55   0.011   .000781  .006009   80.7332
{txt}ln_dur~n {c |}  {res}  .090929      .05314    1.71   0.087  -.013216  .195074   7.75547
{txt}major_~r*{c |}  {res} .0053549       .0746    0.07   0.943  -.140853  .151563    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean,  infantmort_rate1000 = 4.30)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .53441516
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res} -.033964      .01732   -1.96   0.050  -.067914 -.000014   5.64286
 {txt}no_dyad {c |}  {res} .1922863      .13449    1.43   0.153    -.0713  .455872   1.14796
{txt}pre_ac~d*{c |}  {res} .0985329      .09402    1.05   0.295  -.085738  .282803   .744898
{txt}proces~e*{c |}  {res} .4132653      .11236    3.68   0.000   .193043  .633488   .265306
{txt}terri_~o*{c |}  {res} .0234734      .16778    0.14   0.889  -.305361  .352308   .204082
 {txt}polity2 {c |}  {res}-.0123384      .01117   -1.10   0.269  -.034236  .009559   .285714
{txt}mediat~1*{c |}  {res}-.0023175      .11293   -0.02   0.984  -.223656  .219021   .663265
{txt}ethnic~a {c |}  {res} .1429351      .21224    0.67   0.501  -.273041  .558911   .617573
  {txt}ln_pop {c |}  {res}-.0233632      .03109   -0.75   0.452  -.084294  .037568   16.1947
{txt}inf~1000 {c |}  {res} .0045552      .00161    2.83   0.005   .001406  .007704       4.3
{txt}ln_dur~n {c |}  {res} .1220159      .04928    2.48   0.013   .025431  .218601   7.75547
{txt}major_~r*{c |}  {res} .0071809      .09988    0.07   0.943  -.188572  .202934    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean,  infantmort_rate1000 = 161.30)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .95312001
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0060993      .00372   -1.64   0.101  -.013388  .001189   5.64286
 {txt}no_dyad {c |}  {res} .0345307      .02999    1.15   0.250  -.024245  .093307   1.14796
{txt}pre_ac~d*{c |}  {res}  .019378      .02179    0.89   0.374  -.023327  .062083   .744898
{txt}proces~e*{c |}  {res} .0632882      .03455    1.83   0.067  -.004433  .131009   .265306
{txt}terri_~o*{c |}  {res} .0041203      .02795    0.15   0.883  -.050658  .058899   .204082
 {txt}polity2 {c |}  {res}-.0022157      .00202   -1.10   0.273  -.006179  .001748   .285714
{txt}mediat~1*{c |}  {res}-.0004156      .02021   -0.02   0.984  -.040018  .039186   .663265
{txt}ethnic~a {c |}  {res} .0256683      .04185    0.61   0.540  -.056357  .107693   .617573
  {txt}ln_pop {c |}  {res}-.0041956      .00609   -0.69   0.491  -.016133  .007742   16.1947
{txt}inf~1000 {c |}  {res}  .000818      .00022    3.76   0.000   .000392  .001244     161.3
{txt}ln_dur~n {c |}  {res} .0219116      .01089    2.01   0.044   .000561  .043262   7.75547
{txt}major_~r*{c |}  {res} .0012912      .01803    0.07   0.943  -.034041  .036623    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. //Change in provisions
. mfx compute, at (mean, total_prov=0)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .90949144
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0112365      .00227   -4.95   0.000  -.015686 -.006787         0
 {txt}no_dyad {c |}  {res} .0636149       .0489    1.30   0.193  -.032228  .159458   1.14796
{txt}pre_ac~d*{c |}  {res} .0353304      .03861    0.91   0.360  -.040351  .111012   .744898
{txt}proces~e*{c |}  {res} .1174416      .04748    2.47   0.013   .024376  .210507   .265306
{txt}terri_~o*{c |}  {res} .0076084      .05252    0.14   0.885  -.095333   .11055   .204082
 {txt}polity2 {c |}  {res} -.004082      .00335   -1.22   0.223  -.010641  .002477   .285714
{txt}mediat~1*{c |}  {res}-.0007658      .03732   -0.02   0.984   -.07392  .072388   .663265
{txt}ethnic~a {c |}  {res} .0472879      .07122    0.66   0.507  -.092296  .186872   .617573
  {txt}ln_pop {c |}  {res}-.0077294       .0112   -0.69   0.490   -.02968  .014222   16.1947
{txt}inf~1000 {c |}  {res}  .001507      .00064    2.34   0.019   .000243  .002771   80.7332
{txt}ln_dur~n {c |}  {res}  .040367       .0228    1.77   0.077  -.004322  .085056   7.75547
{txt}major_~r*{c |}  {res} .0023784      .03307    0.07   0.943  -.062446  .067202    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, total_prov=3)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res}  .8696574
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0154731      .00529   -2.92   0.003   -.02585 -.005096         3
 {txt}no_dyad {c |}  {res} .0876002      .06083    1.44   0.150  -.031633  .206834   1.14796
{txt}pre_ac~d*{c |}  {res} .0482015      .05024    0.96   0.337   -.05026  .146663   .744898
{txt}proces~e*{c |}  {res} .1630752      .04398    3.71   0.000   .076885  .249265   .265306
{txt}terri_~o*{c |}  {res} .0104994      .07292    0.14   0.886  -.132431   .15343   .204082
 {txt}polity2 {c |}  {res} -.005621      .00465   -1.21   0.227  -.014738  .003496   .285714
{txt}mediat~1*{c |}  {res}-.0010547      .05137   -0.02   0.984  -.101747  .099637   .663265
{txt}ethnic~a {c |}  {res} .0651172      .09686    0.67   0.501   -.12472  .254954   .617573
  {txt}ln_pop {c |}  {res}-.0106436      .01461   -0.73   0.466  -.039279  .017992   16.1947
{txt}inf~1000 {c |}  {res} .0020752      .00066    3.15   0.002   .000785  .003366   80.7332
{txt}ln_dur~n {c |}  {res}  .055587      .02417    2.30   0.021   .008211  .102963   7.75547
{txt}major_~r*{c |}  {res} .0032747      .04558    0.07   0.943  -.086058  .092608    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, total_prov=6)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .81584172
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0205087      .00995   -2.06   0.039  -.040013 -.001005         6
 {txt}no_dyad {c |}  {res} .1161095      .07791    1.49   0.136  -.036597  .268817   1.14796
{txt}pre_ac~d*{c |}  {res} .0631105      .06432    0.98   0.327  -.062959   .18918   .744898
{txt}proces~e*{c |}  {res} .2191829       .0502    4.37   0.000   .120785  .317581   .265306
{txt}terri_~o*{c |}  {res} .0139569      .09762    0.14   0.886  -.177384  .205297   .204082
 {txt}polity2 {c |}  {res}-.0074504      .00647   -1.15   0.249  -.020127  .005226   .285714
{txt}mediat~1*{c |}  {res}-.0013982      .06808   -0.02   0.984  -.134829  .132033   .663265
{txt}ethnic~a {c |}  {res} .0863095      .12862    0.67   0.502  -.165771   .33839   .617573
  {txt}ln_pop {c |}  {res}-.0141076      .01873   -0.75   0.451  -.050812  .022597   16.1947
{txt}inf~1000 {c |}  {res} .0027506      .00082    3.37   0.001   .001149  .004352   80.7332
{txt}ln_dur~n {c |}  {res} .0736777      .02731    2.70   0.007   .020145  .127211   7.75547
{txt}major_~r*{c |}  {res} .0043398      .06045    0.07   0.943  -.114137  .122817    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, total_prov=9)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .74628889
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0258457      .01519   -1.70   0.089  -.055609  .003917         9
 {txt}no_dyad {c |}  {res} .1463245      .09954    1.47   0.142  -.048765  .341414   1.14796
{txt}pre_ac~d*{c |}  {res} .0783219      .07896    0.99   0.321  -.076433  .233077   .744898
{txt}proces~e*{c |}  {res} .2825888      .07612    3.71   0.000   .133404  .431774   .265306
{txt}terri_~o*{c |}  {res} .0176553       .1244    0.14   0.887  -.226172  .261482   .204082
 {txt}polity2 {c |}  {res}-.0093892      .00858   -1.09   0.274  -.026204  .007425   .285714
{txt}mediat~1*{c |}  {res}-.0017624       .0858   -0.02   0.984  -.169922  .166397   .663265
{txt}ethnic~a {c |}  {res} .1087697      .16331    0.67   0.505  -.211313  .428853   .617573
  {txt}ln_pop {c |}  {res}-.0177788      .02326   -0.76   0.445  -.063358    .0278   16.1947
{txt}inf~1000 {c |}  {res} .0034664      .00115    3.01   0.003   .001206  .005726   80.7332
{txt}ln_dur~n {c |}  {res} .0928507      .03391    2.74   0.006   .026392  .159309   7.75547
{txt}major_~r*{c |}  {res} .0054679      .07619    0.07   0.943   -.14387  .154806    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, total_prov=12)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .66137065
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0305711       .0191   -1.60   0.109  -.068003  .006861        12
 {txt}no_dyad {c |}  {res} .1730774      .12028    1.44   0.150  -.062666  .408821   1.14796
{txt}pre_ac~d*{c |}  {res} .0909863      .09022    1.01   0.313  -.085837  .267809   .744898
{txt}proces~e*{c |}  {res} .3463818       .1088    3.18   0.001    .13314  .559623   .265306
{txt}terri_~o*{c |}  {res} .0209806      .14885    0.14   0.888  -.270751  .312712   .204082
 {txt}polity2 {c |}  {res}-.0111059      .01042   -1.07   0.286   -.03152  .009308   .285714
{txt}mediat~1*{c |}  {res}-.0020851      .10152   -0.02   0.984  -.201062  .196891   .663265
{txt}ethnic~a {c |}  {res} .1286563      .19388    0.66   0.507  -.251336  .508648   .617573
  {txt}ln_pop {c |}  {res}-.0210293      .02746   -0.77   0.444  -.074841  .032783   16.1947
{txt}inf~1000 {c |}  {res} .0041001      .00147    2.80   0.005   .001225  .006975   80.7332
{txt}ln_dur~n {c |}  {res} .1098268      .04139    2.65   0.008   .028703  .190951   7.75547
{txt}major_~r*{c |}  {res}  .006466      .09009    0.07   0.943  -.170101  .183033    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, total_prov=15)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .56461173
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0335558      .01947   -1.72   0.085  -.071721  .004609        15
 {txt}no_dyad {c |}  {res} .1899754       .1332    1.43   0.154  -.071093  .451044   1.14796
{txt}pre_ac~d*{c |}  {res} .0979281      .09431    1.04   0.299  -.086916  .282772   .744898
{txt}proces~e*{c |}  {res} .4005759      .13036    3.07   0.002   .145078  .656074   .265306
{txt}terri_~o*{c |}  {res} .0231524      .16517    0.14   0.889  -.300574  .346879   .204082
 {txt}polity2 {c |}  {res}-.0121901       .0113   -1.08   0.281  -.034331  .009951   .285714
{txt}mediat~1*{c |}  {res}-.0022894      .11151   -0.02   0.984  -.220848   .21627   .663265
{txt}ethnic~a {c |}  {res} .1412173      .21168    0.67   0.505  -.273659  .556094   .617573
  {txt}ln_pop {c |}  {res}-.0230825      .03038   -0.76   0.447  -.082625   .03646   16.1947
{txt}inf~1000 {c |}  {res} .0045004      .00155    2.90   0.004   .001457  .007544   80.7332
{txt}ln_dur~n {c |}  {res} .1205495      .04604    2.62   0.009   .030315  .210784   7.75547
{txt}major_~r*{c |}  {res} .0070952      .09877    0.07   0.943  -.186484  .200675    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, total_prov=18)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .46266757
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0339354      .01527   -2.22   0.026  -.063865 -.004006        18
 {txt}no_dyad {c |}  {res} .1921245      .13717    1.40   0.161  -.076726  .460976   1.14796
{txt}pre_ac~d*{c |}  {res} .0971059      .09113    1.07   0.287  -.081498  .275709   .744898
{txt}proces~e*{c |}  {res} .4346799      .13215    3.29   0.001   .175671  .693689   .265306
{txt}terri_~o*{c |}  {res}  .023548      .16866    0.14   0.889  -.307027  .354123   .204082
 {txt}polity2 {c |}  {res}-.0123281      .01096   -1.12   0.261  -.033816   .00916   .285714
{txt}mediat~1*{c |}  {res} -.002316      .11289   -0.02   0.984  -.223579  .218947   .663265
{txt}ethnic~a {c |}  {res} .1428149      .21198    0.67   0.500  -.272654  .558284   .617573
  {txt}ln_pop {c |}  {res}-.0233436      .03147   -0.74   0.458  -.085026  .038339   16.1947
{txt}inf~1000 {c |}  {res} .0045513      .00147    3.09   0.002   .001663  .007439   80.7332
{txt}ln_dur~n {c |}  {res} .1219132       .0498    2.45   0.014   .024298  .219528   7.75547
{txt}major_~r*{c |}  {res} .0071733       .0997    0.07   0.943  -.188238  .202584    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, total_prov=21)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .36375194
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0315917      .00781   -4.05   0.000  -.046896 -.016287        21
 {txt}no_dyad {c |}  {res} .1788556      .13849    1.29   0.197  -.092585  .450296   1.14796
{txt}pre_ac~d*{c |}  {res} .0887723      .08476    1.05   0.295  -.077348  .254892   .744898
{txt}proces~e*{c |}  {res} .4412879      .12907    3.42   0.001   .188313  .694263   .265306
{txt}terri_~o*{c |}  {res} .0220444      .15832    0.14   0.889  -.288259  .332348   .204082
 {txt}polity2 {c |}  {res}-.0114766       .0099   -1.16   0.246  -.030884  .007931   .285714
{txt}mediat~1*{c |}  {res}-.0021567      .10523   -0.02   0.984  -.208408  .204095   .663265
{txt}ethnic~a {c |}  {res} .1329515       .1975    0.67   0.501  -.254133  .520035   .617573
  {txt}ln_pop {c |}  {res}-.0217314      .03087   -0.70   0.481  -.082237  .038774   16.1947
{txt}inf~1000 {c |}  {res}  .004237      .00164    2.58   0.010   .001021  .007453   80.7332
{txt}ln_dur~n {c |}  {res} .1134934      .05866    1.93   0.053  -.001486  .228473   7.75547
{txt}major_~r*{c |}  {res}  .006676      .09261    0.07   0.943   -.17484  .188192    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. logit   pa_lead_pa pam_ucdptotal no_dyad pre_accord process terri_inco polity mediation_1  ethnic_fra  ln_pop infantmort_rate1000  ln_duration major_war, cl(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-111.30276}  
Iteration 1:{space 3}log pseudolikelihood = {res: -84.48948}  
Iteration 2:{space 3}log pseudolikelihood = {res:-82.116306}  
Iteration 3:{space 3}log pseudolikelihood = {res:-82.023067}  
Iteration 4:{space 3}log pseudolikelihood = {res:-82.022759}  
Iteration 5:{space 3}log pseudolikelihood = {res:-82.022759}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}       196
{txt}{col 51}Wald chi2({res}12{txt}){col 67}= {res}     34.47
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0006
{txt}Log pseudolikelihood = {res}-82.022759{txt}{col 51}Pseudo R2{col 67}= {res}    0.2631

{txt}{ralign 85:(Std. Err. adjusted for {res:45} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}         pa_lead_pa{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}pam_ucdptotal {c |}{col 21}{res}{space 2}-.0789614{col 33}{space 2} .0309044{col 44}{space 1}   -2.56{col 53}{space 3}0.011{col 61}{space 4}-.1395328{col 74}{space 3}  -.01839
{txt}{space 12}no_dyad {c |}{col 21}{res}{space 2} .8057377{col 33}{space 2} .5231896{col 44}{space 1}    1.54{col 53}{space 3}0.124{col 61}{space 4}-.2196951{col 74}{space 3}  1.83117
{txt}{space 9}pre_accord {c |}{col 21}{res}{space 2} .4825616{col 33}{space 2} .3951816{col 44}{space 1}    1.22{col 53}{space 3}0.222{col 61}{space 4}-.2919801{col 74}{space 3} 1.257103
{txt}{space 6}process_agree {c |}{col 21}{res}{space 2}  1.80012{col 33}{space 2}  .681863{col 44}{space 1}    2.64{col 53}{space 3}0.008{col 61}{space 4} .4636929{col 74}{space 3} 3.136547
{txt}{space 9}terri_inco {c |}{col 21}{res}{space 2}-.0481219{col 33}{space 2} .6911115{col 44}{space 1}   -0.07{col 53}{space 3}0.944{col 61}{space 4}-1.402675{col 74}{space 3} 1.306432
{txt}{space 12}polity2 {c |}{col 21}{res}{space 2} -.049754{col 33}{space 2} .0405539{col 44}{space 1}   -1.23{col 53}{space 3}0.220{col 61}{space 4}-.1292382{col 74}{space 3} .0297303
{txt}{space 8}mediation_1 {c |}{col 21}{res}{space 2} .0547021{col 33}{space 2} .4753808{col 44}{space 1}    0.12{col 53}{space 3}0.908{col 61}{space 4} -.877027{col 74}{space 3} .9864313
{txt}{space 9}ethnic_fra {c |}{col 21}{res}{space 2} .4486149{col 33}{space 2}  .825134{col 44}{space 1}    0.54{col 53}{space 3}0.587{col 61}{space 4}-1.168618{col 74}{space 3} 2.065848
{txt}{space 13}ln_pop {c |}{col 21}{res}{space 2}-.0758331{col 33}{space 2} .1213191{col 44}{space 1}   -0.63{col 53}{space 3}0.532{col 61}{space 4}-.3136142{col 74}{space 3}  .161948
{txt}infantmort_rate1000 {c |}{col 21}{res}{space 2} .0150745{col 33}{space 2} .0057094{col 44}{space 1}    2.64{col 53}{space 3}0.008{col 61}{space 4} .0038843{col 74}{space 3} .0262647
{txt}{space 8}ln_duration {c |}{col 21}{res}{space 2} .5314426{col 33}{space 2} .1888791{col 44}{space 1}    2.81{col 53}{space 3}0.005{col 61}{space 4} .1612464{col 74}{space 3} .9016388
{txt}{space 10}major_war {c |}{col 21}{res}{space 2}-.1485076{col 33}{space 2} .3861404{col 44}{space 1}   -0.38{col 53}{space 3}0.701{col 61}{space 4} -.905329{col 74}{space 3} .6083137
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-3.940087{col 33}{space 2}  1.85795{col 44}{space 1}   -2.12{col 53}{space 3}0.034{col 61}{space 4}-7.581602{col 74}{space 3}-.2985709
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. mfx compute, at (mean, pam_ucdptotal=0)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .89471937
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0074379      .00291   -2.56   0.011  -.013144 -.001732         0
 {txt}no_dyad {c |}  {res} .0758978      .04928    1.54   0.124  -.020695   .17249   1.14796
{txt}pre_ac~d*{c |}  {res} .0500157      .04593    1.09   0.276  -.040002  .140033   .744898
{txt}proces~e*{c |}  {res} .1290531      .04045    3.19   0.001   .049781  .208326   .265306
{txt}terri_~o*{c |}  {res}-.0045843      .06664   -0.07   0.945  -.135201  .126033   .204082
 {txt}polity2 {c |}  {res}-.0046867      .00382   -1.23   0.220  -.012174    .0028   .285714
{txt}mediat~1*{c |}  {res} .0051895      .04511    0.12   0.908  -.083229  .093608   .663265
{txt}ethnic~a {c |}  {res}  .042258      .07772    0.54   0.587   -.11008  .194596   .617573
  {txt}ln_pop {c |}  {res}-.0071432      .01143   -0.63   0.532  -.029541  .015255   16.1947
{txt}inf~1000 {c |}  {res}   .00142      .00054    2.64   0.008   .000366  .002474   80.7332
{txt}ln_dur~n {c |}  {res} .0500601      .01779    2.81   0.005   .015189  .084931   7.75547
{txt}major_~r*{c |}  {res} -.013911      .03577   -0.39   0.697  -.084021  .056199    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, pam_ucdptotal=43)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .22175652
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0136272      .00249   -5.47   0.000  -.018509 -.008745        43
 {txt}no_dyad {c |}  {res} .1390547      .12297    1.13   0.258  -.101962  .380071   1.14796
{txt}pre_ac~d*{c |}  {res} .0778195      .07248    1.07   0.283  -.064232  .219871   .744898
{txt}proces~e*{c |}  {res} .3665642      .16449    2.23   0.026   .044169  .688959   .265306
{txt}terri_~o*{c |}  {res} -.008239      .11652   -0.07   0.944   -.23662  .220142   .204082
 {txt}polity2 {c |}  {res}-.0085866      .00808   -1.06   0.288  -.024431  .007258   .285714
{txt}mediat~1*{c |}  {res} .0093936      .07995    0.12   0.906  -.147296  .166083   .663265
{txt}ethnic~a {c |}  {res} .0774222      .14917    0.52   0.604  -.214943  .369788   .617573
  {txt}ln_pop {c |}  {res}-.0130873      .02299   -0.57   0.569  -.058157  .031982   16.1947
{txt}inf~1000 {c |}  {res} .0026016      .00176    1.48   0.140  -.000852  .006055   80.7332
{txt}ln_dur~n {c |}  {res} .0917167      .05779    1.59   0.112  -.021547   .20498   7.75547
{txt}major_~r*{c |}  {res}-.0257366      .06781   -0.38   0.704  -.158633   .10716    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean, no_dyad=1)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .80527427
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0123817      .00412   -3.00   0.003   -.02046 -.004303   7.61224
 {txt}no_dyad {c |}  {res} .1263458      .08225    1.54   0.124  -.034854  .287545         1
{txt}pre_ac~d*{c |}  {res} .0811402      .07039    1.15   0.249  -.056823  .219103   .744898
{txt}proces~e*{c |}  {res} .2199621      .05482    4.01   0.000   .112523  .327401   .265306
{txt}terri_~o*{c |}  {res}-.0076116      .11044   -0.07   0.945  -.224065  .208842   .204082
 {txt}polity2 {c |}  {res}-.0078018      .00606   -1.29   0.198  -.019688  .004084   .285714
{txt}mediat~1*{c |}  {res} .0086246      .07517    0.11   0.909  -.138701   .15595   .663265
{txt}ethnic~a {c |}  {res} .0703462      .12927    0.54   0.586  -.183009  .323701   .617573
  {txt}ln_pop {c |}  {res}-.0118912      .01924   -0.62   0.537  -.049607  .025825   16.1947
{txt}inf~1000 {c |}  {res} .0023638      .00085    2.78   0.005   .000699  .004029   80.7332
{txt}ln_dur~n {c |}  {res} .0833342       .0284    2.93   0.003   .027666  .139003   7.75547
{txt}major_~r*{c |}  {res}-.0231808      .05985   -0.39   0.699  -.140492   .09413    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, no_dyad=3)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .95395803
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0034682      .00351   -0.99   0.323  -.010348  .003411   7.61224
 {txt}no_dyad {c |}  {res} .0353897      .01303    2.72   0.007    .00985  .060929         3
{txt}pre_ac~d*{c |}  {res} .0237424      .03116    0.76   0.446  -.037336  .084821   .744898
{txt}proces~e*{c |}  {res} .0594957       .0573    1.04   0.299   -.05281  .171802   .265306
{txt}terri_~o*{c |}  {res}-.0021413      .03153   -0.07   0.946  -.063932   .05965   .204082
 {txt}polity2 {c |}  {res}-.0021853      .00237   -0.92   0.357  -.006839  .002469   .285714
{txt}mediat~1*{c |}  {res} .0024224      .02181    0.11   0.912  -.040329  .045174   .663265
{txt}ethnic~a {c |}  {res} .0197041      .04094    0.48   0.630  -.060532   .09994   .617573
  {txt}ln_pop {c |}  {res}-.0033307      .00536   -0.62   0.535  -.013844  .007182   16.1947
{txt}inf~1000 {c |}  {res} .0006621      .00068    0.97   0.332  -.000675     .002   80.7332
{txt}ln_dur~n {c |}  {res} .0233421      .02156    1.08   0.279   -.01892  .065605   7.75547
{txt}major_~r*{c |}  {res}-.0064824      .01705   -0.38   0.704  -.039902  .026937    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean, pre_accord=0)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .76483056
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0142024      .00543   -2.61   0.009  -.024849 -.003556   7.61224
 {txt}no_dyad {c |}  {res} .1449238      .09084    1.60   0.111  -.033129  .322976   1.14796
{txt}pre_ac~d*{c |}  {res} .0756636      .06612    1.14   0.252  -.053924  .205251         0
{txt}proces~e*{c |}  {res} .2556948      .07862    3.25   0.001   .101596  .409794   .265306
{txt}terri_~o*{c |}  {res}-.0087206       .1261   -0.07   0.945  -.255881   .23844   .204082
 {txt}polity2 {c |}  {res} -.008949      .00748   -1.20   0.232   -.02361  .005712   .285714
{txt}mediat~1*{c |}  {res} .0098854      .08591    0.12   0.908  -.158501  .178272   .663265
{txt}ethnic~a {c |}  {res}   .08069      .14887    0.54   0.588  -.211082  .372462   .617573
  {txt}ln_pop {c |}  {res}-.0136397      .02148   -0.63   0.525   -.05574  .028461   16.1947
{txt}inf~1000 {c |}  {res} .0027114       .0011    2.46   0.014   .000549  .004874   80.7332
{txt}ln_dur~n {c |}  {res} .0955878      .03338    2.86   0.004    .03016  .161016   7.75547
{txt}major_~r*{c |}  {res}-.0266022      .06982   -0.38   0.703  -.163443  .110238    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, pre_accord=1)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .84049418
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0105859      .00358   -2.96   0.003  -.017601 -.003571   7.61224
 {txt}no_dyad {c |}  {res} .1080202      .06552    1.65   0.099  -.020387  .236428   1.14796
{txt}pre_ac~d*{c |}  {res} .0756636      .06612    1.14   0.252  -.053924  .205251         1
{txt}proces~e*{c |}  {res} .1861416      .04806    3.87   0.000   .091953   .28033   .265306
{txt}terri_~o*{c |}  {res}-.0065142      .09477   -0.07   0.945  -.192258   .17923   .204082
 {txt}polity2 {c |}  {res}-.0066702      .00497   -1.34   0.180  -.016421   .00308   .285714
{txt}mediat~1*{c |}  {res} .0073784      .06453    0.11   0.909    -.1191  .133857   .663265
{txt}ethnic~a {c |}  {res}  .060143      .11068    0.54   0.587  -.156782  .277068   .617573
  {txt}ln_pop {c |}  {res}-.0101665      .01649   -0.62   0.538  -.042488  .022155   16.1947
{txt}inf~1000 {c |}  {res} .0020209      .00074    2.73   0.006    .00057  .003472   80.7332
{txt}ln_dur~n {c |}  {res} .0712472      .02471    2.88   0.004   .022818  .119676   7.75547
{txt}major_~r*{c |}  {res}-.0198105      .05066   -0.39   0.696    -.1191  .079479    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean, process=0)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .74292445
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0150807      .00564   -2.67   0.008  -.026137 -.004025   7.61224
 {txt}no_dyad {c |}  {res}  .153886      .09586    1.61   0.108  -.033987  .341759   1.14796
{txt}pre_ac~d*{c |}  {res} .0971459      .08326    1.17   0.243   -.06605  .260342   .744898
{txt}proces~e*{c |}  {res} .2029777      .05043    4.03   0.000   .104138  .301817         0
{txt}terri_~o*{c |}  {res} -.009254      .13375   -0.07   0.945  -.271399  .252891   .204082
 {txt}polity2 {c |}  {res}-.0095024       .0076   -1.25   0.211  -.024406  .005401   .285714
{txt}mediat~1*{c |}  {res} .0104925      .09132    0.11   0.909  -.168499  .189484   .663265
{txt}ethnic~a {c |}  {res} .0856799      .15871    0.54   0.589  -.225383  .396743   .617573
  {txt}ln_pop {c |}  {res}-.0144832      .02306   -0.63   0.530  -.059677  .030711   16.1947
{txt}inf~1000 {c |}  {res}  .002879      .00106    2.71   0.007   .000798   .00496   80.7332
{txt}ln_dur~n {c |}  {res}  .101499      .03282    3.09   0.002   .037167  .165831   7.75547
{txt}major_~r*{c |}  {res}-.0282549      .07292   -0.39   0.698  -.171181  .114672    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, process=1)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .94590212
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0040406      .00215   -1.88   0.060  -.008257  .000176   7.61224
 {txt}no_dyad {c |}  {res} .0412306        .031    1.33   0.183  -.019523  .101984   1.14796
{txt}pre_ac~d*{c |}  {res} .0275926      .02841    0.97   0.331  -.028083  .083268   .744898
{txt}proces~e*{c |}  {res} .2029777      .05043    4.03   0.000   .104138  .301817         1
{txt}terri_~o*{c |}  {res}-.0024941      .03678   -0.07   0.946  -.074576  .069588   .204082
 {txt}polity2 {c |}  {res} -.002546      .00207   -1.23   0.219  -.006604  .001512   .285714
{txt}mediat~1*{c |}  {res} .0028218      .02494    0.11   0.910   -.04606  .051703   .663265
{txt}ethnic~a {c |}  {res} .0229562      .04234    0.54   0.588  -.060025  .105937   .617573
  {txt}ln_pop {c |}  {res}-.0038805      .00676   -0.57   0.566  -.017135  .009374   16.1947
{txt}inf~1000 {c |}  {res} .0007714      .00049    1.58   0.114  -.000185  .001728   80.7332
{txt}ln_dur~n {c |}  {res} .0271946      .01772    1.54   0.125  -.007528  .061917   7.75547
{txt}major_~r*{c |}  {res} -.007553      .01978   -0.38   0.703   -.04632  .031214    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean, terri_inco=0)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .82471548
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0114146       .0037   -3.09   0.002   -.01866  -.00417   7.61224
 {txt}no_dyad {c |}  {res} .1164773      .07178    1.62   0.105  -.024205   .25716   1.14796
{txt}pre_ac~d*{c |}  {res}  .075214      .06726    1.12   0.263  -.056615  .207043   .744898
{txt}proces~e*{c |}  {res} .2016068      .05852    3.45   0.001   .086914  .316299   .265306
{txt}terri_~o*{c |}  {res}-.0070655      .10262   -0.07   0.945  -.208197  .194065         0
 {txt}polity2 {c |}  {res}-.0071924      .00577   -1.25   0.213   -.01851  .004125   .285714
{txt}mediat~1*{c |}  {res} .0079538        .069    0.12   0.908  -.127283  .143191   .663265
{txt}ethnic~a {c |}  {res} .0648517      .12236    0.53   0.596   -.17496  .304664   .617573
  {txt}ln_pop {c |}  {res}-.0109624      .01796   -0.61   0.542  -.046155   .02423   16.1947
{txt}inf~1000 {c |}  {res} .0021792      .00088    2.49   0.013   .000464  .003894   80.7332
{txt}ln_dur~n {c |}  {res} .0768253      .02812    2.73   0.006   .021712  .131938   7.75547
{txt}major_~r*{c |}  {res}-.0213654      .05484   -0.39   0.697  -.128847  .086116    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, terri_inco=1)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .81764995
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res} -.011773      .00639   -1.84   0.066  -.024304  .000758   7.61224
 {txt}no_dyad {c |}  {res} .1201343      .08106    1.48   0.138  -.038745  .279014   1.14796
{txt}pre_ac~d*{c |}  {res} .0774204      .06751    1.15   0.251  -.054901  .209742   .744898
{txt}proces~e*{c |}  {res} .2083668      .08102    2.57   0.010   .049565  .367169   .265306
{txt}terri_~o*{c |}  {res}-.0070655      .10262   -0.07   0.945  -.208197  .194065         1
 {txt}polity2 {c |}  {res}-.0074182      .00537   -1.38   0.167  -.017935  .003098   .285714
{txt}mediat~1*{c |}  {res} .0082024      .07334    0.11   0.911  -.135538  .151943   .663265
{txt}ethnic~a {c |}  {res} .0668878      .11296    0.59   0.554  -.154517  .288292   .617573
  {txt}ln_pop {c |}  {res}-.0113066       .0172   -0.66   0.511  -.045017  .022404   16.1947
{txt}inf~1000 {c |}  {res} .0022476      .00095    2.37   0.018   .000392  .004103   80.7332
{txt}ln_dur~n {c |}  {res} .0792373      .03268    2.42   0.015    .01519  .143284   7.75547
{txt}major_~r*{c |}  {res} -.022038      .05813   -0.38   0.705  -.135971  .091895    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean, mediation_1=0)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .81795079
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0117579      .00512   -2.29   0.022  -.021802 -.001714   7.61224
 {txt}no_dyad {c |}  {res} .1199802      .06973    1.72   0.085  -.016682  .256643   1.14796
{txt}pre_ac~d*{c |}  {res} .0773277      .06582    1.17   0.240  -.051669  .206324   .744898
{txt}proces~e*{c |}  {res}  .208081      .05897    3.53   0.000     .0925  .323662   .265306
{txt}terri_~o*{c |}  {res}-.0072307      .10586   -0.07   0.946  -.214709  .200248   .204082
 {txt}polity2 {c |}  {res}-.0074087      .00537   -1.38   0.168  -.017933  .003115   .285714
{txt}mediat~1*{c |}  {res} .0080043      .06987    0.11   0.909  -.128941   .14495         0
{txt}ethnic~a {c |}  {res}  .066802      .11893    0.56   0.574    -.1663  .299904   .617573
  {txt}ln_pop {c |}  {res}-.0112921      .01736   -0.65   0.515  -.045321  .022736   16.1947
{txt}inf~1000 {c |}  {res} .0022447      .00079    2.86   0.004   .000705  .003785   80.7332
{txt}ln_dur~n {c |}  {res} .0791357      .02731    2.90   0.004   .025607  .132664   7.75547
{txt}major_~r*{c |}  {res}-.0220096      .05639   -0.39   0.696   -.13253  .088511    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, mediation_1=1)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .82595512
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res} -.011351      .00366   -3.10   0.002  -.018524 -.004178   7.61224
 {txt}no_dyad {c |}  {res} .1158274      .07406    1.56   0.118  -.029329  .260984   1.14796
{txt}pre_ac~d*{c |}  {res} .0748207      .06742    1.11   0.267  -.057326  .206967   .744898
{txt}proces~e*{c |}  {res} .2004103      .05955    3.37   0.001   .083697  .317124   .265306
{txt}terri_~o*{c |}  {res}-.0069821      .10099   -0.07   0.945  -.204919  .190955   .204082
 {txt}polity2 {c |}  {res}-.0071523      .00577   -1.24   0.215  -.018469  .004165   .285714
{txt}mediat~1*{c |}  {res} .0080043      .06987    0.11   0.909  -.128941   .14495         1
{txt}ethnic~a {c |}  {res} .0644898      .12104    0.53   0.594  -.172739  .301719   .617573
  {txt}ln_pop {c |}  {res}-.0109013      .01796   -0.61   0.544  -.046111  .024309   16.1947
{txt}inf~1000 {c |}  {res}  .002167      .00089    2.43   0.015   .000416  .003918   80.7332
{txt}ln_dur~n {c |}  {res} .0763966      .02822    2.71   0.007   .021083   .13171   7.75547
{txt}major_~r*{c |}  {res}-.0212459        .055   -0.39   0.699  -.129035  .086543    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean,  ln_duration = 2.64)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .23509376
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0141992       .0087   -1.63   0.103  -.031244  .002846   7.61224
 {txt}no_dyad {c |}  {res} .1448915      .10438    1.39   0.165   -.05969  .349473   1.14796
{txt}pre_ac~d*{c |}  {res} .0812995      .07822    1.04   0.299  -.072006  .234605   .744898
{txt}proces~e*{c |}  {res} .3755137      .19576    1.92   0.055  -.008177  .759204   .265306
{txt}terri_~o*{c |}  {res}-.0085881      .12372   -0.07   0.945  -.251069  .233892   .204082
 {txt}polity2 {c |}  {res} -.008947      .00847   -1.06   0.291  -.025539  .007645   .285714
{txt}mediat~1*{c |}  {res} .0097901      .08581    0.11   0.909  -.158385  .177965   .663265
{txt}ethnic~a {c |}  {res}  .080672      .14731    0.55   0.584  -.208045  .369389   .617573
  {txt}ln_pop {c |}  {res}-.0136367      .01901   -0.72   0.473    -.0509  .023626   16.1947
{txt}inf~1000 {c |}  {res} .0027108      .00117    2.31   0.021   .000412  .005009   80.7332
{txt}ln_dur~n {c |}  {res} .0955665      .01516    6.30   0.000   .065847  .125286      2.64
{txt}major_~r*{c |}  {res}-.0268104      .07094   -0.38   0.705  -.165852  .112231    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean,  ln_duration = 9.81)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .93280984
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res} -.004949      .00237   -2.08   0.037  -.009603 -.000295   7.61224
 {txt}no_dyad {c |}  {res} .0505001      .03131    1.61   0.107  -.010866  .111866   1.14796
{txt}pre_ac~d*{c |}  {res} .0336614      .03402    0.99   0.322  -.033013  .100336   .744898
{txt}proces~e*{c |}  {res} .0852111      .04176    2.04   0.041   .003371  .167051   .265306
{txt}terri_~o*{c |}  {res}-.0030536      .04488   -0.07   0.946  -.091016  .084909   .204082
 {txt}polity2 {c |}  {res}-.0031184      .00257   -1.21   0.225  -.008156   .00192   .285714
{txt}mediat~1*{c |}  {res} .0034553      .03045    0.11   0.910  -.056232  .063142   .663265
{txt}ethnic~a {c |}  {res} .0281172       .0515    0.55   0.585  -.072821  .129055   .617573
  {txt}ln_pop {c |}  {res}-.0047529      .00704   -0.68   0.499  -.018543  .009037   16.1947
{txt}inf~1000 {c |}  {res} .0009448      .00038    2.50   0.012   .000204  .001685   80.7332
{txt}ln_dur~n {c |}  {res} .0333085      .00636    5.24   0.000   .020853  .045764      9.81
{txt}major_~r*{c |}  {res}-.0092523      .02395   -0.39   0.699   -.05619  .037686    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean,  major_war = 0)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .83488309
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0108851       .0039   -2.79   0.005  -.018524 -.003246   7.61224
 {txt}no_dyad {c |}  {res} .1110736      .06687    1.66   0.097  -.019997  .242144   1.14796
{txt}pre_ac~d*{c |}  {res} .0719326      .06136    1.17   0.241   -.04834  .192205   .744898
{txt}proces~e*{c |}  {res} .1916998      .05697    3.36   0.001   .080035  .303364   .265306
{txt}terri_~o*{c |}  {res}-.0066973      .09722   -0.07   0.945  -.197251  .183856   .204082
 {txt}polity2 {c |}  {res}-.0068588      .00476   -1.44   0.150  -.016192  .002474   .285714
{txt}mediat~1*{c |}  {res} .0075862      .06635    0.11   0.909  -.122464  .137636   .663265
{txt}ethnic~a {c |}  {res}  .061843      .11501    0.54   0.591  -.163573  .287259   .617573
  {txt}ln_pop {c |}  {res}-.0104538      .01702   -0.61   0.539  -.043812  .022904   16.1947
{txt}inf~1000 {c |}  {res} .0020781      .00082    2.54   0.011   .000475  .003681   80.7332
{txt}ln_dur~n {c |}  {res} .0732611      .02649    2.77   0.006   .021334  .125188   7.75547
{txt}major_~r*{c |}  {res}-.0215021      .05538   -0.39   0.698  -.130038  .087033         0
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean,  major_war = 1)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .81338096
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0119857      .00429   -2.79   0.005  -.020391  -.00358   7.61224
 {txt}no_dyad {c |}  {res} .1223048      .07658    1.60   0.110  -.027781  .272391   1.14796
{txt}pre_ac~d*{c |}  {res} .0787243      .07102    1.11   0.268  -.060477  .217926   .744898
{txt}proces~e*{c |}  {res} .2124018      .05721    3.71   0.000   .100281  .324523   .265306
{txt}terri_~o*{c |}  {res}-.0073698      .10708   -0.07   0.945  -.217247  .202508   .204082
 {txt}polity2 {c |}  {res}-.0075523       .0063   -1.20   0.231  -.019898  .004793   .285714
{txt}mediat~1*{c |}  {res}   .00835      .07279    0.11   0.909  -.134316  .151016   .663265
{txt}ethnic~a {c |}  {res} .0680963      .12462    0.55   0.585  -.176152  .312344   .617573
  {txt}ln_pop {c |}  {res}-.0115109      .01836   -0.63   0.531  -.047493  .024471   16.1947
{txt}inf~1000 {c |}  {res} .0022882      .00087    2.64   0.008    .00059  .003986   80.7332
{txt}ln_dur~n {c |}  {res} .0806689      .02805    2.88   0.004   .025688   .13565   7.75547
{txt}major_~r*{c |}  {res}-.0215021      .05538   -0.39   0.698  -.130038  .087033         1
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean, polity=-9)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .88088193
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0082853      .00366   -2.26   0.024  -.015463 -.001108   7.61224
 {txt}no_dyad {c |}  {res} .0845452      .05343    1.58   0.114  -.020175  .189265   1.14796
{txt}pre_ac~d*{c |}  {res} .0554871      .04813    1.15   0.249   -.03884  .149815   .744898
{txt}proces~e*{c |}  {res} .1442081      .05479    2.63   0.008   .036822  .251594   .265306
{txt}terri_~o*{c |}  {res}-.0051045      .07484   -0.07   0.946  -.151793  .141584   .204082
 {txt}polity2 {c |}  {res}-.0052206      .00252   -2.07   0.039  -.010169 -.000273        -9
{txt}mediat~1*{c |}  {res} .0057792      .05103    0.11   0.910  -.094235  .105794   .663265
{txt}ethnic~a {c |}  {res} .0470727      .08851    0.53   0.595  -.126405  .220551   .617573
  {txt}ln_pop {c |}  {res}-.0079571      .01328   -0.60   0.549  -.033991  .018077   16.1947
{txt}inf~1000 {c |}  {res} .0015817      .00079    2.01   0.044    .00004  .003123   80.7332
{txt}ln_dur~n {c |}  {res} .0557637      .02582    2.16   0.031   .005149  .106379   7.75547
{txt}major_~r*{c |}  {res}-.0154983      .03771   -0.41   0.681  -.089406  .058409    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, polity=10)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .74182635
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0151227       .0061   -2.48   0.013  -.027074 -.003171   7.61224
 {txt}no_dyad {c |}  {res} .1543149       .1032    1.50   0.135  -.047963  .356593   1.14796
{txt}pre_ac~d*{c |}  {res} .0973878      .08891    1.10   0.273  -.076866  .271642   .744898
{txt}proces~e*{c |}  {res} .2745517      .09638    2.85   0.004   .085656  .463447   .265306
{txt}terri_~o*{c |}  {res}-.0092795      .13375   -0.07   0.945  -.271424  .252865   .204082
 {txt}polity2 {c |}  {res}-.0095289       .0092   -1.04   0.300  -.027563  .008505        10
{txt}mediat~1*{c |}  {res} .0105215      .09114    0.12   0.908  -.168115  .189158   .663265
{txt}ethnic~a {c |}  {res} .0859187      .15792    0.54   0.586   -.22359  .395428   .617573
  {txt}ln_pop {c |}  {res}-.0145236      .02311   -0.63   0.530  -.059811  .030764   16.1947
{txt}inf~1000 {c |}  {res} .0028871      .00115    2.52   0.012   .000638  .005136   80.7332
{txt}ln_dur~n {c |}  {res} .1017819      .03834    2.65   0.008   .026631  .176933   7.75547
{txt}major_~r*{c |}  {res}-.0283341      .07615   -0.37   0.710  -.177588   .12092    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean, ethnic_fra = 0.25)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .79801086
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0127278      .00483   -2.63   0.008  -.022201 -.003255   7.61224
 {txt}no_dyad {c |}  {res} .1298765      .08185    1.59   0.113  -.030538  .290291   1.14796
{txt}pre_ac~d*{c |}  {res} .0832383      .07352    1.13   0.258   -.06085  .227327   .744898
{txt}proces~e*{c |}  {res} .2266228      .07772    2.92   0.004    .07429  .378956   .265306
{txt}terri_~o*{c |}  {res}-.0078226      .11279   -0.07   0.945  -.228882  .213237   .204082
 {txt}polity2 {c |}  {res}-.0080198      .00629   -1.28   0.202  -.020344  .004305   .285714
{txt}mediat~1*{c |}  {res} .0088644      .07683    0.12   0.908  -.141721   .15945   .663265
{txt}ethnic~a {c |}  {res}  .072312      .14597    0.50   0.620  -.213788  .358412       .25
  {txt}ln_pop {c |}  {res}-.0122235      .02062   -0.59   0.553  -.052647    .0282   16.1947
{txt}inf~1000 {c |}  {res} .0024298      .00094    2.58   0.010   .000584  .004275   80.7332
{txt}ln_dur~n {c |}  {res}  .085663      .03429    2.50   0.012   .018451  .152875   7.75547
{txt}major_~r*{c |}  {res}-.0238306      .06111   -0.39   0.697  -.143613  .095952    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, ethnic_fra = 0.93) 

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .84276761
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0104632      .00399   -2.62   0.009  -.018282 -.002644   7.61224
 {txt}no_dyad {c |}  {res} .1067686       .0671    1.59   0.112  -.024746  .238283   1.14796
{txt}pre_ac~d*{c |}  {res} .0693009      .06202    1.12   0.264   -.05225  .190852   .744898
{txt}proces~e*{c |}  {res} .1838712      .05203    3.53   0.000   .081891  .285851   .265306
{txt}terri_~o*{c |}  {res}-.0064391      .09418   -0.07   0.945  -.191034  .178155   .204082
 {txt}polity2 {c |}  {res}-.0065929      .00521   -1.26   0.206  -.016811  .003626   .285714
{txt}mediat~1*{c |}  {res} .0072932      .06412    0.11   0.909  -.118388  .132975   .663265
{txt}ethnic~a {c |}  {res} .0594461      .09886    0.60   0.548  -.134315  .253207       .93
  {txt}ln_pop {c |}  {res}-.0100487      .01547   -0.65   0.516  -.040375  .020277   16.1947
{txt}inf~1000 {c |}  {res} .0019975      .00085    2.36   0.018    .00034  .003655   80.7332
{txt}ln_dur~n {c |}  {res} .0704217      .02476    2.84   0.004   .021895  .118949   7.75547
{txt}major_~r*{c |}  {res}-.0195805      .05088   -0.38   0.700  -.119304  .080144    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean, ln_pop = 13.21)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .85385271
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0098535      .00441   -2.23   0.025  -.018495 -.001212   7.61224
 {txt}no_dyad {c |}  {res} .1005466      .05668    1.77   0.076  -.010543  .211636   1.14796
{txt}pre_ac~d*{c |}  {res}  .065471      .06326    1.03   0.301  -.058517  .189459   .744898
{txt}proces~e*{c |}  {res} .1726476      .06735    2.56   0.010   .040636  .304659   .265306
{txt}terri_~o*{c |}  {res}-.0060659      .08876   -0.07   0.946  -.180041   .16791   .204082
 {txt}polity2 {c |}  {res}-.0062087      .00514   -1.21   0.227  -.016292  .003875   .285714
{txt}mediat~1*{c |}  {res} .0068696      .06069    0.11   0.910  -.112076  .125815   .663265
{txt}ethnic~a {c |}  {res} .0559819      .09778    0.57   0.567  -.135661  .247625   .617573
  {txt}ln_pop {c |}  {res}-.0094631      .01278   -0.74   0.459  -.034518  .015592     13.21
{txt}inf~1000 {c |}  {res} .0018811      .00081    2.33   0.020   .000302   .00346   80.7332
{txt}ln_dur~n {c |}  {res} .0663178      .01902    3.49   0.000   .029037  .103599   7.75547
{txt}major_~r*{c |}  {res}-.0184371      .04821   -0.38   0.702  -.112928  .076054    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, ln_pop = 20.63)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .76896251
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0140282      .00584   -2.40   0.016  -.025481 -.002575   7.61224
 {txt}no_dyad {c |}  {res} .1431467      .10869    1.32   0.188  -.069886  .356179   1.14796
{txt}pre_ac~d*{c |}  {res} .0910087      .07653    1.19   0.234  -.058995  .241013   .744898
{txt}proces~e*{c |}  {res} .2521953      .09589    2.63   0.009   .064263  .440128   .265306
{txt}terri_~o*{c |}  {res}-.0086147      .12401   -0.07   0.945  -.251669   .23444   .204082
 {txt}polity2 {c |}  {res}-.0088393      .00706   -1.25   0.211  -.022677  .004998   .285714
{txt}mediat~1*{c |}  {res} .0097649      .08414    0.12   0.908  -.155138  .174668   .663265
{txt}ethnic~a {c |}  {res} .0797005      .15763    0.51   0.613  -.229248  .388649   .617573
  {txt}ln_pop {c |}  {res}-.0134724      .02548   -0.53   0.597  -.063418  .036473     20.63
{txt}inf~1000 {c |}  {res} .0026781       .0013    2.07   0.039    .00014  .005216   80.7332
{txt}ln_dur~n {c |}  {res} .0944156      .05164    1.83   0.068  -.006803  .195634   7.75547
{txt}major_~r*{c |}  {res}-.0262747      .06733   -0.39   0.696  -.158234  .105684    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean,  infantmort_rate1000 = 4.30)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .59546795
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0190207      .00721   -2.64   0.008  -.033151  -.00489   7.61224
 {txt}no_dyad {c |}  {res} .1940908      .12359    1.57   0.116  -.048135  .436317   1.14796
{txt}pre_ac~d*{c |}  {res} .1179517      .09785    1.21   0.228  -.073832  .309735   .744898
{txt}proces~e*{c |}  {res} .3694524      .11313    3.27   0.001   .147726  .591178   .265306
{txt}terri_~o*{c |}  {res}-.0116224       .1671   -0.07   0.945  -.339126  .315881   .204082
 {txt}polity2 {c |}  {res} -.011985      .00966   -1.24   0.215   -.03091   .00694   .285714
{txt}mediat~1*{c |}  {res} .0131985      .11451    0.12   0.908  -.211244  .237641   .663265
{txt}ethnic~a {c |}  {res}  .108065      .19794    0.55   0.585  -.279891  .496021   .617573
  {txt}ln_pop {c |}  {res}-.0182671      .02939   -0.62   0.534  -.075864  .039329   16.1947
{txt}inf~1000 {c |}  {res} .0036312      .00164    2.22   0.027   .000419  .006844       4.3
{txt}ln_dur~n {c |}  {res}  .128017       .0503    2.55   0.011   .029428  .226606   7.75547
{txt}major_~r*{c |}  {res}-.0357066      .09259   -0.39   0.700  -.217189  .145776    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean,  infantmort_rate1000 = 161.30)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .94010006
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0044465      .00247   -1.80   0.072  -.009289  .000396   7.61224
 {txt}no_dyad {c |}  {res} .0453727      .03399    1.33   0.182  -.021242  .111987   1.14796
{txt}pre_ac~d*{c |}  {res} .0303108      .02951    1.03   0.304  -.027526  .088148   .744898
{txt}proces~e*{c |}  {res} .0764588      .03861    1.98   0.048   .000788   .15213   .265306
{txt}terri_~o*{c |}  {res}-.0027442      .04045   -0.07   0.946  -.082026  .076538   .204082
 {txt}polity2 {c |}  {res}-.0028017      .00242   -1.16   0.247  -.007548  .001944   .285714
{txt}mediat~1*{c |}  {res} .0031049      .02756    0.11   0.910  -.050915  .057125   .663265
{txt}ethnic~a {c |}  {res} .0252624      .04883    0.52   0.605  -.070441  .120966   .617573
  {txt}ln_pop {c |}  {res}-.0042703        .007   -0.61   0.542  -.017987  .009446   16.1947
{txt}inf~1000 {c |}  {res} .0008489      .00018    4.77   0.000     .0005  .001197     161.3
{txt}ln_dur~n {c |}  {res} .0299266      .01221    2.45   0.014   .006004   .05385   7.75547
{txt}major_~r*{c |}  {res}-.0083122       .0216   -0.38   0.700  -.050652  .034027    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean, pam_ucdptotal=0)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .89471937
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0074379      .00291   -2.56   0.011  -.013144 -.001732         0
 {txt}no_dyad {c |}  {res} .0758978      .04928    1.54   0.124  -.020695   .17249   1.14796
{txt}pre_ac~d*{c |}  {res} .0500157      .04593    1.09   0.276  -.040002  .140033   .744898
{txt}proces~e*{c |}  {res} .1290531      .04045    3.19   0.001   .049781  .208326   .265306
{txt}terri_~o*{c |}  {res}-.0045843      .06664   -0.07   0.945  -.135201  .126033   .204082
 {txt}polity2 {c |}  {res}-.0046867      .00382   -1.23   0.220  -.012174    .0028   .285714
{txt}mediat~1*{c |}  {res} .0051895      .04511    0.12   0.908  -.083229  .093608   .663265
{txt}ethnic~a {c |}  {res}  .042258      .07772    0.54   0.587   -.11008  .194596   .617573
  {txt}ln_pop {c |}  {res}-.0071432      .01143   -0.63   0.532  -.029541  .015255   16.1947
{txt}inf~1000 {c |}  {res}   .00142      .00054    2.64   0.008   .000366  .002474   80.7332
{txt}ln_dur~n {c |}  {res} .0500601      .01779    2.81   0.005   .015189  .084931   7.75547
{txt}major_~r*{c |}  {res} -.013911      .03577   -0.39   0.697  -.084021  .056199    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, pam_ucdptotal=3)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .87023029
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0089171      .00215   -4.16   0.000  -.013123 -.004711         3
 {txt}no_dyad {c |}  {res} .0909916      .05662    1.61   0.108  -.019979  .201962   1.14796
{txt}pre_ac~d*{c |}  {res} .0595318      .05332    1.12   0.264   -.04498  .164044   .744898
{txt}proces~e*{c |}  {res} .1555982      .04198    3.71   0.000   .073311  .237886   .265306
{txt}terri_~o*{c |}  {res} -.005492      .07982   -0.07   0.945  -.161944   .15096   .204082
 {txt}polity2 {c |}  {res}-.0056187      .00428   -1.31   0.190  -.014017  .002779   .285714
{txt}mediat~1*{c |}  {res} .0062187      .05416    0.11   0.909  -.099929  .112367   .663265
{txt}ethnic~a {c |}  {res} .0506619      .09363    0.54   0.588  -.132847   .23417   .617573
  {txt}ln_pop {c |}  {res}-.0085638      .01394   -0.61   0.539   -.03588  .018752   16.1947
{txt}inf~1000 {c |}  {res} .0017024      .00069    2.48   0.013   .000357  .003047   80.7332
{txt}ln_dur~n {c |}  {res} .0600156      .02162    2.78   0.006   .017633  .102398   7.75547
{txt}major_~r*{c |}  {res} -.016682      .04289   -0.39   0.697   -.10074  .067376    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, pam_ucdptotal=6)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res}  .8410568
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0105556       .0032   -3.30   0.001  -.016821  -.00429         6
 {txt}no_dyad {c |}  {res} .1077112      .06533    1.65   0.099  -.020327  .235749   1.14796
{txt}pre_ac~d*{c |}  {res} .0698784      .06149    1.14   0.256  -.050649  .190406   .744898
{txt}proces~e*{c |}  {res} .1855807      .04632    4.01   0.000     .0948  .276361   .265306
{txt}terri_~o*{c |}  {res}-.0064957      .09437   -0.07   0.945  -.191464  .178473   .204082
 {txt}polity2 {c |}  {res}-.0066511      .00507   -1.31   0.190  -.016597  .003295   .285714
{txt}mediat~1*{c |}  {res} .0073574      .06418    0.11   0.909  -.118428  .133143   .663265
{txt}ethnic~a {c |}  {res}  .059971       .1104    0.54   0.587  -.156417  .276359   .617573
  {txt}ln_pop {c |}  {res}-.0101374      .01633   -0.62   0.535  -.042147  .021873   16.1947
{txt}inf~1000 {c |}  {res} .0020152      .00075    2.68   0.007   .000541   .00349   80.7332
{txt}ln_dur~n {c |}  {res} .0710434      .02389    2.97   0.003   .024212  .117875   7.75547
{txt}major_~r*{c |}  {res}-.0197537      .05083   -0.39   0.698   -.11938  .079872    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, pam_ucdptotal=9)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .80678101
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0123089      .00445   -2.77   0.006  -.021033 -.003585         9
 {txt}no_dyad {c |}  {res} .1256027      .07564    1.66   0.097  -.022642  .273848   1.14796
{txt}pre_ac~d*{c |}  {res} .0806971      .07016    1.15   0.250  -.056813  .218207   .744898
{txt}proces~e*{c |}  {res}  .218567      .05509    3.97   0.000   .110595  .326539   .265306
{txt}terri_~o*{c |}  {res}-.0075671      .10987   -0.07   0.945  -.222903  .207769   .204082
 {txt}polity2 {c |}  {res}-.0077559      .00598   -1.30   0.195  -.019474  .003962   .285714
{txt}mediat~1*{c |}  {res} .0085741      .07488    0.11   0.909  -.138197  .155345   .663265
{txt}ethnic~a {c |}  {res} .0699325       .1285    0.54   0.586  -.181921  .321787   .617573
  {txt}ln_pop {c |}  {res}-.0118213      .01891   -0.63   0.532   -.04889  .025248   16.1947
{txt}inf~1000 {c |}  {res} .0023499      .00085    2.78   0.005   .000692  .004008   80.7332
{txt}ln_dur~n {c |}  {res} .0828441      .02734    3.03   0.002   .029264  .136424   7.75547
{txt}major_~r*{c |}  {res}-.0230441       .0594   -0.39   0.698  -.139464  .093376    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, pam_ucdptotal=12)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .76716001
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0141045      .00579   -2.43   0.015  -.025462 -.002747        12
 {txt}no_dyad {c |}  {res} .1439253      .08725    1.65   0.099  -.027088  .314938   1.14796
{txt}pre_ac~d*{c |}  {res} .0914585      .07879    1.16   0.246  -.062976  .245893   .744898
{txt}proces~e*{c |}  {res} .2537261      .06841    3.71   0.000   .119648  .387804   .265306
{txt}terri_~o*{c |}  {res}-.0086611      .12563   -0.07   0.945  -.254888  .237566   .204082
 {txt}polity2 {c |}  {res}-.0088873      .00696   -1.28   0.202  -.022527  .004752   .285714
{txt}mediat~1*{c |}  {res} .0098177      .08582    0.11   0.909  -.158377  .178013   .663265
{txt}ethnic~a {c |}  {res} .0801341      .14717    0.54   0.586  -.208321   .36859   .617573
  {txt}ln_pop {c |}  {res}-.0135457      .02158   -0.63   0.530  -.055843  .028752   16.1947
{txt}inf~1000 {c |}  {res} .0026927      .00097    2.78   0.005   .000797  .004588   80.7332
{txt}ln_dur~n {c |}  {res} .0949292      .03189    2.98   0.003   .032434  .157424   7.75547
{txt}major_~r*{c |}  {res}-.0264182      .06824   -0.39   0.699   -.16017  .107333    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, pam_ucdptotal=15)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .72221184
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0158414      .00707   -2.24   0.025  -.029695 -.001987        15
 {txt}no_dyad {c |}  {res} .1616486      .09939    1.63   0.104  -.033157  .356454   1.14796
{txt}pre_ac~d*{c |}  {res}  .101481      .08666    1.17   0.242   -.06836  .271322   .744898
{txt}proces~e*{c |}  {res} .2897919      .08481    3.42   0.001   .123563  .456021   .265306
{txt}terri_~o*{c |}  {res} -.009715      .14073   -0.07   0.945  -.285542  .266112   .204082
 {txt}polity2 {c |}  {res}-.0099817      .00795   -1.26   0.209  -.025555  .005591   .285714
{txt}mediat~1*{c |}  {res} .0110176      .09633    0.11   0.909  -.177779  .199814   .663265
{txt}ethnic~a {c |}  {res}  .090002      .16535    0.54   0.586  -.234069  .414073   .617573
  {txt}ln_pop {c |}  {res}-.0152138      .02419   -0.63   0.529  -.062621  .032193   16.1947
{txt}inf~1000 {c |}  {res} .0030243       .0011    2.74   0.006   .000861  .005187   80.7332
{txt}ln_dur~n {c |}  {res}  .106619      .03696    2.88   0.004   .034173  .179065   7.75547
{txt}major_~r*{c |}  {res}-.0296879      .07685   -0.39   0.699  -.180318  .120942    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, pam_ucdptotal=18)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .67229326
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0173964      .00808   -2.15   0.031  -.033224 -.001569        18
 {txt}no_dyad {c |}  {res} .1775161      .11088    1.60   0.109  -.039798   .39483   1.14796
{txt}pre_ac~d*{c |}  {res}  .109992      .09289    1.18   0.236  -.072073  .292057   .744898
{txt}proces~e*{c |}  {res} .3250882      .10203    3.19   0.001   .125112  .525065   .265306
{txt}terri_~o*{c |}  {res}-.0106533      .15406   -0.07   0.945  -.312613  .291306   .204082
 {txt}polity2 {c |}  {res}-.0109616      .00884   -1.24   0.215  -.028295  .006372   .285714
{txt}mediat~1*{c |}  {res} .0120881      .10564    0.11   0.909  -.194963  .219139   .663265
{txt}ethnic~a {c |}  {res} .0988366      .18166    0.54   0.586  -.257212  .454886   .617573
  {txt}ln_pop {c |}  {res}-.0167072      .02655   -0.63   0.529   -.06874  .035326   16.1947
{txt}inf~1000 {c |}  {res} .0033211      .00123    2.69   0.007   .000903  .005739   80.7332
{txt}ln_dur~n {c |}  {res} .1170848      .04167    2.81   0.005   .035406  .198764   7.75547
{txt}major_~r*{c |}  {res}-.0326232       .0846   -0.39   0.700  -.198443  .133196    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, pam_ucdptotal=21)

{txt}Marginal effects after logit
      y  = Pr(pa_lead_pa) (predict)
         = {res} .61814718
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0186381      .00861   -2.16   0.031  -.035523 -.001753        21
 {txt}no_dyad {c |}  {res} .1901873      .12044    1.58   0.114  -.045866  .426241   1.14796
{txt}pre_ac~d*{c |}  {res} .1162322      .09676    1.20   0.230  -.073419  .305884   .744898
{txt}proces~e*{c |}  {res} .3576357      .11763    3.04   0.002   .127082  .588189   .265306
{txt}terri_~o*{c |}  {res} -.011396      .16447   -0.07   0.945  -.333747  .310955   .204082
 {txt}polity2 {c |}  {res} -.011744      .00955   -1.23   0.219  -.030468   .00698   .285714
{txt}mediat~1*{c |}  {res} .0129383      .11294    0.11   0.909  -.208416  .234293   .663265
{txt}ethnic~a {c |}  {res} .1058916      .19468    0.54   0.586  -.275679  .487462   .617573
  {txt}ln_pop {c |}  {res}-.0178997      .02846   -0.63   0.529  -.073686  .037886   16.1947
{txt}inf~1000 {c |}  {res} .0035582      .00134    2.66   0.008   .000941  .006176   80.7332
{txt}ln_dur~n {c |}  {res} .1254424      .04514    2.78   0.005   .036963  .213921   7.75547
{txt}major_~r*{c |}  {res}-.0349775      .09082   -0.39   0.700  -.212973  .143018    .55102
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. 
. 
. //Marginal Effect Based on Model 4 and Model 7 in Table 3
. logit   dyvi05 total_p no_dyad pre_accord  terri_inco polity mediation_1  ethnic_fra  ln_pop infantmort_rate1000  ln_duration major_war, cl(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-131.23728}  
Iteration 1:{space 3}log pseudolikelihood = {res:-107.25356}  
Iteration 2:{space 3}log pseudolikelihood = {res:-106.82265}  
Iteration 3:{space 3}log pseudolikelihood = {res:-106.82173}  
Iteration 4:{space 3}log pseudolikelihood = {res:-106.82173}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}       195
{txt}{col 51}Wald chi2({res}11{txt}){col 67}= {res}     37.97
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0001
{txt}Log pseudolikelihood = {res}-106.82173{txt}{col 51}Pseudo R2{col 67}= {res}    0.1860

{txt}{ralign 85:(Std. Err. adjusted for {res:45} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             dyvi05{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}total_prov {c |}{col 21}{res}{space 2}-.2439496{col 33}{space 2} .0595916{col 44}{space 1}   -4.09{col 53}{space 3}0.000{col 61}{space 4} -.360747{col 74}{space 3}-.1271522
{txt}{space 12}no_dyad {c |}{col 21}{res}{space 2} .8001231{col 33}{space 2} .6872247{col 44}{space 1}    1.16{col 53}{space 3}0.244{col 61}{space 4}-.5468125{col 74}{space 3} 2.147059
{txt}{space 9}pre_accord {c |}{col 21}{res}{space 2} -1.19011{col 33}{space 2} .4826995{col 44}{space 1}   -2.47{col 53}{space 3}0.014{col 61}{space 4}-2.136184{col 74}{space 3}-.2440366
{txt}{space 9}terri_inco {c |}{col 21}{res}{space 2}-.1528061{col 33}{space 2} .6612423{col 44}{space 1}   -0.23{col 53}{space 3}0.817{col 61}{space 4}-1.448817{col 74}{space 3} 1.143205
{txt}{space 12}polity2 {c |}{col 21}{res}{space 2}-.0185839{col 33}{space 2}  .068633{col 44}{space 1}   -0.27{col 53}{space 3}0.787{col 61}{space 4}-.1531021{col 74}{space 3} .1159343
{txt}{space 8}mediation_1 {c |}{col 21}{res}{space 2}  .919822{col 33}{space 2}  .482762{col 44}{space 1}    1.91{col 53}{space 3}0.057{col 61}{space 4} -.026374{col 74}{space 3} 1.866018
{txt}{space 9}ethnic_fra {c |}{col 21}{res}{space 2} -.840554{col 33}{space 2} 1.141071{col 44}{space 1}   -0.74{col 53}{space 3}0.461{col 61}{space 4}-3.077013{col 74}{space 3} 1.395905
{txt}{space 13}ln_pop {c |}{col 21}{res}{space 2} .1467166{col 33}{space 2} .1961988{col 44}{space 1}    0.75{col 53}{space 3}0.455{col 61}{space 4} -.237826{col 74}{space 3} .5312593
{txt}infantmort_rate1000 {c |}{col 21}{res}{space 2} .0166272{col 33}{space 2} .0068692{col 44}{space 1}    2.42{col 53}{space 3}0.015{col 61}{space 4} .0031638{col 74}{space 3} .0300906
{txt}{space 8}ln_duration {c |}{col 21}{res}{space 2} .2661566{col 33}{space 2} .2272801{col 44}{space 1}    1.17{col 53}{space 3}0.242{col 61}{space 4}-.1793043{col 74}{space 3} .7116174
{txt}{space 10}major_war {c |}{col 21}{res}{space 2} 1.041485{col 33}{space 2} .3708285{col 44}{space 1}    2.81{col 53}{space 3}0.005{col 61}{space 4} .3146746{col 74}{space 3} 1.768296
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-5.599101{col 33}{space 2} 3.829106{col 44}{space 1}   -1.46{col 53}{space 3}0.144{col 61}{space 4}-13.10401{col 74}{space 3}  1.90581
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. mfx compute, at (mean, total_prov=0)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res}  .6987076
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0513551      .00659   -7.79   0.000  -.064281 -.038429         0
 {txt}no_dyad {c |}  {res} .1684381       .1454    1.16   0.247  -.116536  .453412   1.14872
{txt}pre_ac~d*{c |}  {res}-.2180333       .0852   -2.56   0.010  -.385028 -.051038    .74359
{txt}terri_~o*{c |}  {res}-.0327261      .14335   -0.23   0.819  -.313691  .248239   .205128
 {txt}polity2 {c |}  {res}-.0039122      .01446   -0.27   0.787  -.032247  .024423   .297436
{txt}mediat~1*{c |}  {res} .2020604      .09663    2.09   0.037   .012679  .391442   .661538
{txt}ethnic~a {c |}  {res}-.1769495      .23947   -0.74   0.460  -.646311  .292412   .617075
  {txt}ln_pop {c |}  {res} .0308861      .03955    0.78   0.435  -.046623  .108395   16.1888
{txt}inf~1000 {c |}  {res} .0035003      .00144    2.43   0.015    .00068  .006321   80.8523
{txt}ln_dur~n {c |}  {res}   .05603      .04838    1.16   0.247  -.038799  .150859   7.74787
{txt}major_~r*{c |}  {res} .2206945      .07619    2.90   0.004   .071372  .370017   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, total_prov=21)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .01362957
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0032796      .00217   -1.51   0.130  -.007525  .000965        21
 {txt}no_dyad {c |}  {res} .0107567      .01346    0.80   0.424  -.015624  .037138   1.14872
{txt}pre_ac~d*{c |}  {res}-.0223132      .02119   -1.05   0.292  -.063852  .019226    .74359
{txt}terri_~o*{c |}  {res}-.0019679      .00782   -0.25   0.801  -.017297  .013361   .205128
 {txt}polity2 {c |}  {res}-.0002498      .00099   -0.25   0.800  -.002182  .001682   .297436
{txt}mediat~1*{c |}  {res} .0110522      .00865    1.28   0.202   -.00591  .028014   .661538
{txt}ethnic~a {c |}  {res}-.0113002      .01917   -0.59   0.556  -.048882  .026282   .617075
  {txt}ln_pop {c |}  {res} .0019724      .00287    0.69   0.491  -.003646  .007591   16.1888
{txt}inf~1000 {c |}  {res} .0002235      .00021    1.06   0.288  -.000188  .000635   80.8523
{txt}ln_dur~n {c |}  {res} .0035782      .00422    0.85   0.397    -.0047  .011856   7.74787
{txt}major_~r*{c |}  {res}  .013888      .01269    1.09   0.274  -.010976  .038752   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean, no_dyad=1)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .34209603
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0549048      .01397   -3.93   0.000  -.082283 -.027527   5.64103
 {txt}no_dyad {c |}  {res} .1800808      .15039    1.20   0.231  -.114678  .474839         1
{txt}pre_ac~d*{c |}  {res}-.2804375      .11224   -2.50   0.012  -.500428 -.060447    .74359
{txt}terri_~o*{c |}  {res}-.0338793      .14411   -0.24   0.814  -.316322  .248564   .205128
 {txt}polity2 {c |}  {res}-.0041826       .0155   -0.27   0.787  -.034565    .0262   .297436
{txt}mediat~1*{c |}  {res} .1946178       .0996    1.95   0.051  -.000601  .389836   .661538
{txt}ethnic~a {c |}  {res}-.1891804       .2598   -0.73   0.467  -.698389  .320028   .617075
  {txt}ln_pop {c |}  {res}  .033021      .04458    0.74   0.459  -.054348   .12039   16.1888
{txt}inf~1000 {c |}  {res} .0037422      .00158    2.37   0.018   .000654  .006831   80.8523
{txt}ln_dur~n {c |}  {res} .0599029      .05101    1.17   0.240   -.04008  .159885   7.74787
{txt}major_~r*{c |}  {res} .2271593      .07941    2.86   0.004   .071509   .38281   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, no_dyad=3)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .72036618
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0491409      .03054   -1.61   0.108  -.108992   .01071   5.64103
 {txt}no_dyad {c |}  {res} .1611758      .04824    3.34   0.001   .066618  .255733         3
{txt}pre_ac~d*{c |}  {res}-.2069014      .16872   -1.23   0.220  -.537585  .123782    .74359
{txt}terri_~o*{c |}  {res}-.0313784      .14223   -0.22   0.825  -.310139  .247382   .205128
 {txt}polity2 {c |}  {res}-.0037435      .01384   -0.27   0.787  -.030876  .023389   .297436
{txt}mediat~1*{c |}  {res} .1949618      .15099    1.29   0.197  -.100964  .490888   .661538
{txt}ethnic~a {c |}  {res}-.1693201      .24527   -0.69   0.490  -.650031  .311391   .617075
  {txt}ln_pop {c |}  {res} .0295544      .03681    0.80   0.422  -.042599  .101707   16.1888
{txt}inf~1000 {c |}  {res} .0033494      .00219    1.53   0.126  -.000938  .007636   80.8523
{txt}ln_dur~n {c |}  {res} .0536142      .05567    0.96   0.336  -.055497  .162725   7.74787
{txt}major_~r*{c |}  {res} .2121373      .11984    1.77   0.077  -.022753  .447027   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean, pre_accord=0)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .58661246
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0591574      .01429   -4.14   0.000  -.087156 -.031159   5.64103
 {txt}no_dyad {c |}  {res} .1940285       .1684    1.15   0.249  -.136028  .524085   1.14872
{txt}pre_ac~d*{c |}  {res}-.2851053      .11169   -2.55   0.011   -.50402 -.066191         0
{txt}terri_~o*{c |}  {res}-.0373102       .1623   -0.23   0.818   -.35541  .280789   .205128
 {txt}polity2 {c |}  {res}-.0045066      .01665   -0.27   0.787  -.037136  .028123   .297436
{txt}mediat~1*{c |}  {res} .2238252      .11455    1.95   0.051  -.000684  .448335   .661538
{txt}ethnic~a {c |}  {res}-.2038329      .27932   -0.73   0.466  -.751292  .343626   .617075
  {txt}ln_pop {c |}  {res} .0355785      .04813    0.74   0.460  -.058756  .129914   16.1888
{txt}inf~1000 {c |}  {res} .0040321      .00164    2.46   0.014   .000815  .007249   80.8523
{txt}ln_dur~n {c |}  {res} .0645425      .05377    1.20   0.230  -.040854  .169939   7.74787
{txt}major_~r*{c |}  {res}  .249383      .08536    2.92   0.003   .082072  .416694   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, pre_accord=1)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .30150714
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0513759      .01333   -3.85   0.000  -.077503 -.025249   5.64103
 {txt}no_dyad {c |}  {res} .1685064       .1484    1.14   0.256   -.12235  .459362   1.14872
{txt}pre_ac~d*{c |}  {res}-.2851053      .11169   -2.55   0.011   -.50402 -.066191         1
{txt}terri_~o*{c |}  {res}-.0315897      .13343   -0.24   0.813  -.293113  .229934   .205128
 {txt}polity2 {c |}  {res}-.0039138      .01455   -0.27   0.788  -.032434  .024607   .297436
{txt}mediat~1*{c |}  {res} .1805825      .09227    1.96   0.050  -.000255   .36142   .661538
{txt}ethnic~a {c |}  {res}-.1770212      .24689   -0.72   0.473  -.660923  .306881   .617075
  {txt}ln_pop {c |}  {res} .0308986      .04288    0.72   0.471  -.053152  .114949   16.1888
{txt}inf~1000 {c |}  {res} .0035017       .0015    2.33   0.020   .000555  .006448   80.8523
{txt}ln_dur~n {c |}  {res} .0560527       .0467    1.20   0.230  -.035471  .147577   7.74787
{txt}major_~r*{c |}  {res} .2125304      .07778    2.73   0.006   .060089  .364972   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean, terri_inco=0)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .37668799
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0572779      .01489   -3.85   0.000  -.086455 -.028101   5.64103
 {txt}no_dyad {c |}  {res} .1878642      .16168    1.16   0.245  -.129031  .504759   1.14872
{txt}pre_ac~d*{c |}  {res}-.2860432       .1123   -2.55   0.011  -.506138 -.065949    .74359
{txt}terri_~o*{c |}  {res}-.0351473      .14973   -0.23   0.814  -.328618  .258323         0
 {txt}polity2 {c |}  {res}-.0043634      .01611   -0.27   0.786  -.035937   .02721   .297436
{txt}mediat~1*{c |}  {res} .2045968      .10741    1.90   0.057  -.005915  .415109   .661538
{txt}ethnic~a {c |}  {res}-.1973572      .27071   -0.73   0.466  -.727943  .333229   .617075
  {txt}ln_pop {c |}  {res} .0344482      .04682    0.74   0.462  -.057322  .126219   16.1888
{txt}inf~1000 {c |}  {res}  .003904      .00161    2.43   0.015   .000755  .007053   80.8523
{txt}ln_dur~n {c |}  {res}  .062492      .05242    1.19   0.233  -.040245  .165229   7.74787
{txt}major_~r*{c |}  {res} .2371619      .08596    2.76   0.006   .068685  .405638   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, terri_inco=1)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .34154066
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res} -.054862      .01495   -3.67   0.000  -.084169 -.025555   5.64103
 {txt}no_dyad {c |}  {res} .1799402      .16348    1.10   0.271  -.140465  .500345   1.14872
{txt}pre_ac~d*{c |}  {res}-.2803226      .11061   -2.53   0.011  -.497122 -.063523    .74359
{txt}terri_~o*{c |}  {res}-.0351473      .14973   -0.23   0.814  -.328618  .258323         1
 {txt}polity2 {c |}  {res}-.0041793      .01577   -0.27   0.791  -.035079   .02672   .297436
{txt}mediat~1*{c |}  {res} .1944426      .08548    2.27   0.023   .026901  .361985   .661538
{txt}ethnic~a {c |}  {res}-.1890327      .26023   -0.73   0.468   -.69908  .321015   .617075
  {txt}ln_pop {c |}  {res} .0329952      .04458    0.74   0.459  -.054383  .120373   16.1888
{txt}inf~1000 {c |}  {res} .0037393      .00189    1.97   0.048   .000025  .007453   80.8523
{txt}ln_dur~n {c |}  {res} .0598561      .05563    1.08   0.282  -.049178   .16889   7.74787
{txt}major_~r*{c |}  {res} .2269803      .07794    2.91   0.004   .074228  .379732   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean, mediation_1=0)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .24168338
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0447093      .00975   -4.59   0.000   -.06381 -.025608   5.64103
 {txt}no_dyad {c |}  {res} .1466406      .13443    1.09   0.275  -.116844  .410126   1.14872
{txt}pre_ac~d*{c |}  {res}-.2455152      .10682   -2.30   0.022  -.454886 -.036145    .74359
{txt}terri_~o*{c |}  {res}-.0273487      .11252   -0.24   0.808  -.247883  .193186   .205128
 {txt}polity2 {c |}  {res}-.0034059      .01284   -0.27   0.791  -.028569  .021757   .297436
{txt}mediat~1*{c |}  {res} .2026359      .10194    1.99   0.047   .002832  .402439         0
{txt}ethnic~a {c |}  {res}-.1540504      .21137   -0.73   0.466   -.56833  .260229   .617075
  {txt}ln_pop {c |}  {res} .0268891      .03553    0.76   0.449   -.04275  .096528   16.1888
{txt}inf~1000 {c |}  {res} .0030473      .00136    2.25   0.025   .000389  .005705   80.8523
{txt}ln_dur~n {c |}  {res} .0487792      .04374    1.12   0.265  -.036941  .134499   7.74787
{txt}major_~r*{c |}  {res} .1851983      .06483    2.86   0.004    .05814  .312256   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, mediation_1=1)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .44431929
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0602311      .01546   -3.90   0.000  -.090526 -.029936   5.64103
 {txt}no_dyad {c |}  {res} .1975501      .16954    1.17   0.244  -.134744  .529844   1.14872
{txt}pre_ac~d*{c |}  {res}-.2887579       .1099   -2.63   0.009  -.504167 -.073349    .74359
{txt}terri_~o*{c |}  {res}-.0375029      .16145   -0.23   0.816   -.35394  .278935   .205128
 {txt}polity2 {c |}  {res}-.0045884      .01694   -0.27   0.787  -.037795  .028619   .297436
{txt}mediat~1*{c |}  {res} .2026359      .10194    1.99   0.047   .002832  .402439         1
{txt}ethnic~a {c |}  {res}-.2075325      .28335   -0.73   0.464  -.762891  .347826   .617075
  {txt}ln_pop {c |}  {res} .0362243      .04898    0.74   0.460   -.05977  .132219   16.1888
{txt}inf~1000 {c |}  {res} .0041053      .00172    2.39   0.017   .000736  .007474   80.8523
{txt}ln_dur~n {c |}  {res}  .065714      .05579    1.18   0.239  -.043627  .175055   7.74787
{txt}major_~r*{c |}  {res} .2502109      .08788    2.85   0.004   .077962   .42246   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean,  ln_duration = 2.64)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .13073593
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0277234      .02483   -1.12   0.264  -.076391  .020944   5.64103
 {txt}no_dyad {c |}  {res} .0909292      .11306    0.80   0.421  -.130671  .312529   1.14872
{txt}pre_ac~d*{c |}  {res}-.1672908      .11618   -1.44   0.150  -.394998  .060416    .74359
{txt}terri_~o*{c |}  {res}-.0167989      .07673   -0.22   0.827  -.167182  .133584   .205128
 {txt}polity2 {c |}  {res} -.002112      .00789   -0.27   0.789   -.01758  .013356   .297436
{txt}mediat~1*{c |}  {res} .0947002      .10537    0.90   0.369  -.111824  .301224   .661538
{txt}ethnic~a {c |}  {res} -.095524      .17984   -0.53   0.595  -.447996  .256948   .617075
  {txt}ln_pop {c |}  {res} .0166735      .03052    0.55   0.585  -.043137  .076484   16.1888
{txt}inf~1000 {c |}  {res} .0018896      .00152    1.24   0.214  -.001093  .004872   80.8523
{txt}ln_dur~n {c |}  {res} .0302471        .004    7.56   0.000   .022407  .038087      2.64
{txt}major_~r*{c |}  {res} .1156827      .12091    0.96   0.339  -.121305   .35267   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean,  ln_duration = 9.81)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .50346873
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0609845      .01488   -4.10   0.000  -.090148 -.031821   5.64103
 {txt}no_dyad {c |}  {res} .2000211      .17177    1.16   0.244  -.136651  .536693   1.14872
{txt}pre_ac~d*{c |}  {res}-.2830186      .10018   -2.83   0.005  -.479371 -.086667    .74359
{txt}terri_~o*{c |}  {res}-.0381737      .16478   -0.23   0.817  -.361144  .284796   .205128
 {txt}polity2 {c |}  {res}-.0046458      .01715   -0.27   0.787  -.038268  .028976   .297436
{txt}mediat~1*{c |}  {res} .2250159      .11233    2.00   0.045   .004851  .445181   .661538
{txt}ethnic~a {c |}  {res}-.2101284      .28543   -0.74   0.462  -.769564  .349307   .617075
  {txt}ln_pop {c |}  {res} .0366774      .04906    0.75   0.455  -.059486  .132841   16.1888
{txt}inf~1000 {c |}  {res} .0041566      .00171    2.43   0.015   .000804  .007509   80.8523
{txt}ln_dur~n {c |}  {res} .0665359      .05661    1.18   0.240  -.044414  .177486      9.81
{txt}major_~r*{c |}  {res} .2545619      .08606    2.96   0.003   .085879  .423244   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean,  major_war = 0)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .24853212
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res} -.045561      .01182   -3.85   0.000  -.068737 -.022385   5.64103
 {txt}no_dyad {c |}  {res} .1494341      .12839    1.16   0.244  -.102208  .401076   1.14872
{txt}pre_ac~d*{c |}  {res}-.2488698      .10576   -2.35   0.019  -.456148 -.041592    .74359
{txt}terri_~o*{c |}  {res}-.0278861      .11637   -0.24   0.811  -.255966  .200193   .205128
 {txt}polity2 {c |}  {res}-.0034708      .01302   -0.27   0.790  -.028998  .022056   .297436
{txt}mediat~1*{c |}  {res} .1585455      .07693    2.06   0.039    .00776  .309331   .661538
{txt}ethnic~a {c |}  {res}-.1569851      .22123   -0.71   0.478  -.590581  .276611   .617075
  {txt}ln_pop {c |}  {res} .0274014      .03661    0.75   0.454  -.044361  .099164   16.1888
{txt}inf~1000 {c |}  {res} .0031054      .00137    2.27   0.023   .000419  .005791   80.8523
{txt}ln_dur~n {c |}  {res} .0497084       .0442    1.12   0.261  -.036914  .136331   7.74787
{txt}major_~r*{c |}  {res} .2352308      .08258    2.85   0.004   .073374  .397087         0
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean,  major_war = 1)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .48376289
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0609231      .01497   -4.07   0.000  -.090271 -.031575   5.64103
 {txt}no_dyad {c |}  {res} .1998198      .17197    1.16   0.245  -.137239  .536879   1.14872
{txt}pre_ac~d*{c |}  {res}-.2857225      .10773   -2.65   0.008   -.49687 -.074575    .74359
{txt}terri_~o*{c |}  {res}-.0380678      .16422   -0.23   0.817  -.359931  .283796   .205128
 {txt}polity2 {c |}  {res}-.0046411      .01714   -0.27   0.787  -.038231  .028949   .297436
{txt}mediat~1*{c |}  {res} .2235581      .11388    1.96   0.050   .000353  .446763   .661538
{txt}ethnic~a {c |}  {res}-.2099169      .28503   -0.74   0.461  -.768573  .348739   .617075
  {txt}ln_pop {c |}  {res} .0366405      .04916    0.75   0.456  -.059714  .132995   16.1888
{txt}inf~1000 {c |}  {res} .0041524      .00172    2.41   0.016   .000782  .007523   80.8523
{txt}ln_dur~n {c |}  {res}  .066469      .05661    1.17   0.240   -.04448  .177418   7.74787
{txt}major_~r*{c |}  {res} .2352308      .08258    2.85   0.004   .073374  .397087         1
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean, polity=-9)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .41042901
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0590302      .01556   -3.79   0.000  -.089536 -.028524   5.64103
 {txt}no_dyad {c |}  {res} .1936114       .1696    1.14   0.254  -.138792  .526015   1.14872
{txt}pre_ac~d*{c |}  {res}-.2887025      .11055   -2.61   0.009  -.505374 -.072031    .74359
{txt}terri_~o*{c |}  {res}-.0366447      .15511   -0.24   0.813  -.340648  .267359   .205128
 {txt}polity2 {c |}  {res}-.0044969      .01716   -0.26   0.793  -.038125  .029131        -9
{txt}mediat~1*{c |}  {res} .2125428      .10255    2.07   0.038   .011548  .413537   .661538
{txt}ethnic~a {c |}  {res}-.2033948      .29082   -0.70   0.484  -.773398  .366608   .617075
  {txt}ln_pop {c |}  {res} .0355021      .04852    0.73   0.464  -.059601  .130605   16.1888
{txt}inf~1000 {c |}  {res} .0040234      .00165    2.44   0.015   .000794  .007252   80.8523
{txt}ln_dur~n {c |}  {res} .0644038       .0562    1.15   0.252  -.045755  .174563   7.74787
{txt}major_~r*{c |}  {res} .2447468      .08048    3.04   0.002   .087018  .402476   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, polity=10)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res}  .3284318
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0538066      .01894   -2.84   0.005  -.090938 -.016675   5.64103
 {txt}no_dyad {c |}  {res} .1764786      .15633    1.13   0.259  -.129913  .482871   1.14872
{txt}pre_ac~d*{c |}  {res}-.2773742      .12028   -2.31   0.021  -.513112 -.041637    .74359
{txt}terri_~o*{c |}  {res}-.0331619      .14381   -0.23   0.818  -.315031  .248707   .205128
 {txt}polity2 {c |}  {res}-.0040989      .01426   -0.29   0.774  -.032058   .02386        10
{txt}mediat~1*{c |}  {res} .1901714      .11883    1.60   0.110  -.042727   .42307   .661538
{txt}ethnic~a {c |}  {res}-.1853962      .23528   -0.79   0.431  -.646542   .27575   .617075
  {txt}ln_pop {c |}  {res} .0323605      .04389    0.74   0.461  -.053653  .118374   16.1888
{txt}inf~1000 {c |}  {res} .0036674      .00191    1.92   0.055  -.000083  .007418   80.8523
{txt}ln_dur~n {c |}  {res} .0587047      .05006    1.17   0.241  -.039411   .15682   7.74787
{txt}major_~r*{c |}  {res} .2225845      .10618    2.10   0.036   .014468  .430701   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean, ethnic_fra = 0.25)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .44363358
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0602123      .01503   -4.01   0.000  -.089671 -.030754   5.64103
 {txt}no_dyad {c |}  {res} .1974886      .17056    1.16   0.247  -.136812   .53179   1.14872
{txt}pre_ac~d*{c |}  {res}-.2887817      .11111   -2.60   0.009  -.506552 -.071012    .74359
{txt}terri_~o*{c |}  {res}-.0374889      .16105   -0.23   0.816  -.353144  .278166   .205128
 {txt}polity2 {c |}  {res}-.0045869      .01708   -0.27   0.788  -.038059  .028885   .297436
{txt}mediat~1*{c |}  {res} .2186068      .11324    1.93   0.054  -.003336  .440549   .661538
{txt}ethnic~a {c |}  {res}-.2074679      .29257   -0.71   0.478  -.780894  .365958       .25
  {txt}ln_pop {c |}  {res}  .036213      .04912    0.74   0.461  -.060067  .132494   16.1888
{txt}inf~1000 {c |}  {res}  .004104      .00173    2.37   0.018   .000708    .0075   80.8523
{txt}ln_dur~n {c |}  {res} .0656935      .05498    1.19   0.232   -.04207  .173457   7.74787
{txt}major_~r*{c |}  {res} .2501217      .08426    2.97   0.003   .084979  .415265   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, ethnic_fra = 0.93) 

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .31045246
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0522227      .01498   -3.49   0.000  -.081585  -.02286   5.64103
 {txt}no_dyad {c |}  {res} .1712837      .14996    1.14   0.253   -.12263  .465198   1.14872
{txt}pre_ac~d*{c |}  {res}-.2725755      .11875   -2.30   0.022  -.505326 -.039825    .74359
{txt}terri_~o*{c |}  {res}-.0321354      .13592   -0.24   0.813  -.298531   .23426   .205128
 {txt}polity2 {c |}  {res}-.0039783      .01448   -0.27   0.784  -.032366   .02441   .297436
{txt}mediat~1*{c |}  {res} .1838896      .09276    1.98   0.047   .002091  .365688   .661538
{txt}ethnic~a {c |}  {res}-.1799388      .22344   -0.81   0.421  -.617874  .257997       .93
  {txt}ln_pop {c |}  {res} .0314079      .04209    0.75   0.456  -.051096  .113911   16.1888
{txt}inf~1000 {c |}  {res} .0035594      .00155    2.29   0.022   .000512  .006607   80.8523
{txt}ln_dur~n {c |}  {res} .0569766      .05161    1.10   0.270  -.044171  .158124   7.74787
{txt}major_~r*{c |}  {res} .2160242      .08736    2.47   0.013   .044799  .387249   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean, ln_pop = 13.21)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .27447788
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0485801       .0142   -3.42   0.001  -.076408 -.020752   5.64103
 {txt}no_dyad {c |}  {res} .1593363       .1301    1.22   0.221  -.095651  .414324   1.14872
{txt}pre_ac~d*{c |}  {res}-.2602159      .13241   -1.97   0.049  -.519728 -.000704    .74359
{txt}terri_~o*{c |}  {res}-.0298007      .12493   -0.24   0.811  -.274665  .215064   .205128
 {txt}polity2 {c |}  {res}-.0037008      .01367   -0.27   0.787  -.030497  .023096   .297436
{txt}mediat~1*{c |}  {res} .1698611      .07906    2.15   0.032     .0149  .324822   .661538
{txt}ethnic~a {c |}  {res}-.1673877       .2247   -0.74   0.456  -.607784  .273008   .617075
  {txt}ln_pop {c |}  {res} .0292171      .03235    0.90   0.366   -.03419  .092625     13.21
{txt}inf~1000 {c |}  {res} .0033111      .00154    2.14   0.032   .000285  .006337   80.8523
{txt}ln_dur~n {c |}  {res} .0530024      .05043    1.05   0.293  -.045844  .151849   7.74787
{txt}major_~r*{c |}  {res}   .20104      .06763    2.97   0.003   .068481  .333599   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, ln_pop = 20.63)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .52912113
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0607805      .01433   -4.24   0.000  -.088872 -.032689   5.64103
 {txt}no_dyad {c |}  {res} .1993522      .16803    1.19   0.235  -.129977  .528681   1.14872
{txt}pre_ac~d*{c |}  {res} -.278363      .13021   -2.14   0.033  -.533564 -.023162    .74359
{txt}terri_~o*{c |}  {res}-.0381342      .16503   -0.23   0.817  -.361581  .285313   .205128
 {txt}polity2 {c |}  {res}-.0046302      .01707   -0.27   0.786   -.03808  .028819   .297436
{txt}mediat~1*{c |}  {res} .2259313      .11551    1.96   0.050  -.000472  .452335   .661538
{txt}ethnic~a {c |}  {res}-.2094257       .2816   -0.74   0.457  -.761353  .342501   .617075
  {txt}ln_pop {c |}  {res} .0365547      .04688    0.78   0.436  -.055333  .128443     20.63
{txt}inf~1000 {c |}  {res} .0041427       .0017    2.44   0.015   .000812  .007473   80.8523
{txt}ln_dur~n {c |}  {res} .0663134      .05775    1.15   0.251  -.046882  .179509   7.74787
{txt}major_~r*{c |}  {res} .2543846      .08402    3.03   0.002   .089703  .419066   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean,  infantmort_rate1000 = 4.30)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .14090092
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0295296      .01251   -2.36   0.018  -.054052 -.005007   5.64103
 {txt}no_dyad {c |}  {res} .0968532      .08752    1.11   0.268  -.074685  .268391   1.14872
{txt}pre_ac~d*{c |}  {res}-.1765315      .09287   -1.90   0.057  -.358553   .00549    .74359
{txt}terri_~o*{c |}  {res}-.0179087       .0771   -0.23   0.816  -.169021  .133203   .205128
 {txt}polity2 {c |}  {res}-.0022495      .00862   -0.26   0.794  -.019145  .014646   .297436
{txt}mediat~1*{c |}  {res} .1010114      .06187    1.63   0.103  -.020257   .22228   .661538
{txt}ethnic~a {c |}  {res}-.1017473      .14483   -0.70   0.482  -.385611  .182116   .617075
  {txt}ln_pop {c |}  {res} .0177597      .02488    0.71   0.475  -.031005  .066524   16.1888
{txt}inf~1000 {c |}  {res} .0020127      .00028    7.25   0.000   .001469  .002557       4.3
{txt}ln_dur~n {c |}  {res} .0322177      .02441    1.32   0.187  -.015635   .08007   7.74787
{txt}major_~r*{c |}  {res}  .123104      .06466    1.90   0.057   -.00362  .249828   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean,  infantmort_rate1000 = 161.30)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .69054044
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0521306      .01607   -3.24   0.001  -.083634 -.020627   5.64103
 {txt}no_dyad {c |}  {res} .1709818      .14564    1.17   0.240  -.114476   .45644   1.14872
{txt}pre_ac~d*{c |}  {res}-.2220389       .0982   -2.26   0.024  -.414511 -.029567    .74359
{txt}terri_~o*{c |}  {res} -.033195      .14862   -0.22   0.823   -.32448   .25809   .205128
 {txt}polity2 {c |}  {res}-.0039713      .01483   -0.27   0.789  -.033035  .025092   .297436
{txt}mediat~1*{c |}  {res} .2044833      .11028    1.85   0.064  -.011669  .420635   .661538
{txt}ethnic~a {c |}  {res}-.1796216      .24213   -0.74   0.458   -.65418  .294937   .617075
  {txt}ln_pop {c |}  {res} .0313525      .04104    0.76   0.445  -.049081  .111786   16.1888
{txt}inf~1000 {c |}  {res} .0035531      .00073    4.90   0.000   .002131  .004976     161.3
{txt}ln_dur~n {c |}  {res} .0568762      .04533    1.25   0.210  -.031962  .145715   7.74787
{txt}major_~r*{c |}  {res} .2236607      .08826    2.53   0.011   .050684  .396638   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. //Model 7
. logit   dyvi05 pam_ucdptotal no_dyad pre_accord  terri_inco polity mediation_1  ethnic_fra  ln_pop infantmort_rate1000  ln_duration major_war, cl(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-131.23728}  
Iteration 1:{space 3}log pseudolikelihood = {res:-108.47707}  
Iteration 2:{space 3}log pseudolikelihood = {res:-107.66891}  
Iteration 3:{space 3}log pseudolikelihood = {res:-107.66157}  
Iteration 4:{space 3}log pseudolikelihood = {res:-107.66157}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}       195
{txt}{col 51}Wald chi2({res}11{txt}){col 67}= {res}     34.56
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0003
{txt}Log pseudolikelihood = {res}-107.66157{txt}{col 51}Pseudo R2{col 67}= {res}    0.1796

{txt}{ralign 85:(Std. Err. adjusted for {res:45} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             dyvi05{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}pam_ucdptotal {c |}{col 21}{res}{space 2}-.1300308{col 33}{space 2} .0409884{col 44}{space 1}   -3.17{col 53}{space 3}0.002{col 61}{space 4}-.2103666{col 74}{space 3} -.049695
{txt}{space 12}no_dyad {c |}{col 21}{res}{space 2} .8195771{col 33}{space 2} .7401455{col 44}{space 1}    1.11{col 53}{space 3}0.268{col 61}{space 4}-.6310814{col 74}{space 3} 2.270236
{txt}{space 9}pre_accord {c |}{col 21}{res}{space 2}-1.230839{col 33}{space 2} .4878598{col 44}{space 1}   -2.52{col 53}{space 3}0.012{col 61}{space 4}-2.187027{col 74}{space 3}-.2746516
{txt}{space 9}terri_inco {c |}{col 21}{res}{space 2}-.2840445{col 33}{space 2} .6741466{col 44}{space 1}   -0.42{col 53}{space 3}0.674{col 61}{space 4}-1.605348{col 74}{space 3} 1.037259
{txt}{space 12}polity2 {c |}{col 21}{res}{space 2} -.015959{col 33}{space 2} .0658831{col 44}{space 1}   -0.24{col 53}{space 3}0.809{col 61}{space 4}-.1450875{col 74}{space 3} .1131694
{txt}{space 8}mediation_1 {c |}{col 21}{res}{space 2} .9055836{col 33}{space 2} .4468452{col 44}{space 1}    2.03{col 53}{space 3}0.043{col 61}{space 4}  .029783{col 74}{space 3} 1.781384
{txt}{space 9}ethnic_fra {c |}{col 21}{res}{space 2}-1.017989{col 33}{space 2} 1.158531{col 44}{space 1}   -0.88{col 53}{space 3}0.380{col 61}{space 4}-3.288669{col 74}{space 3}  1.25269
{txt}{space 13}ln_pop {c |}{col 21}{res}{space 2} .1363196{col 33}{space 2} .2069795{col 44}{space 1}    0.66{col 53}{space 3}0.510{col 61}{space 4}-.2693527{col 74}{space 3} .5419919
{txt}infantmort_rate1000 {c |}{col 21}{res}{space 2} .0133024{col 33}{space 2} .0071453{col 44}{space 1}    1.86{col 53}{space 3}0.063{col 61}{space 4}-.0007021{col 74}{space 3}  .027307
{txt}{space 8}ln_duration {c |}{col 21}{res}{space 2} .3051005{col 33}{space 2} .2260954{col 44}{space 1}    1.35{col 53}{space 3}0.177{col 61}{space 4}-.1380383{col 74}{space 3} .7482393
{txt}{space 10}major_war {c |}{col 21}{res}{space 2} .8493793{col 33}{space 2} .3687578{col 44}{space 1}    2.30{col 53}{space 3}0.021{col 61}{space 4} .1266272{col 74}{space 3} 1.572131
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-5.644286{col 33}{space 2} 4.176164{col 44}{space 1}   -1.35{col 53}{space 3}0.177{col 61}{space 4}-13.82942{col 74}{space 3} 2.540846
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. mfx compute, at (mean, pam_ucdptotal=0)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .59920768
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0312279      .00984   -3.17   0.002  -.050521 -.011935         0
 {txt}no_dyad {c |}  {res} .1968279      .17775    1.11   0.268  -.151559  .545215   1.14872
{txt}pre_ac~d*{c |}  {res}-.2671218      .09843   -2.71   0.007  -.460034 -.074209    .74359
{txt}terri_~o*{c |}  {res}-.0691338      .16599   -0.42   0.677  -.394477  .256209   .205128
 {txt}polity2 {c |}  {res}-.0038327      .01582   -0.24   0.809  -.034844  .027179   .297436
{txt}mediat~1*{c |}  {res} .2191772      .10334    2.12   0.034   .016643  .421711   .661538
{txt}ethnic~a {c |}  {res}-.2444782      .27823   -0.88   0.380    -.7898  .300843   .617075
  {txt}ln_pop {c |}  {res} .0327382      .04971    0.66   0.510  -.064687  .130164   16.1888
{txt}inf~1000 {c |}  {res} .0031947      .00172    1.86   0.063  -.000169  .006558   80.8523
{txt}ln_dur~n {c |}  {res} .0732723       .0543    1.35   0.177  -.033151  .179696   7.74787
{txt}major_~r*{c |}  {res} .2028296      .08439    2.40   0.016   .037437  .368222   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, pam_ucdptotal=43)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .00554577
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0007171      .00086   -0.84   0.402  -.002395  .000961        43
 {txt}no_dyad {c |}  {res}   .00452      .00649    0.70   0.486  -.008191  .017231   1.14872
{txt}pre_ac~d*{c |}  {res}-.0096849      .01492   -0.65   0.516  -.038933  .019563    .74359
{txt}terri_~o*{c |}  {res}-.0014466      .00391   -0.37   0.711  -.009112  .006219   .205128
 {txt}polity2 {c |}  {res} -.000088       .0004   -0.22   0.826  -.000874  .000698   .297436
{txt}mediat~1*{c |}  {res} .0044659      .00665    0.67   0.502  -.008576  .017508   .661538
{txt}ethnic~a {c |}  {res}-.0056142      .01074   -0.52   0.601  -.026662  .015433   .617075
  {txt}ln_pop {c |}  {res} .0007518      .00137    0.55   0.584  -.001937   .00344   16.1888
{txt}inf~1000 {c |}  {res} .0000734      .00011    0.66   0.508  -.000144  .000291   80.8523
{txt}ln_dur~n {c |}  {res} .0016826      .00271    0.62   0.535  -.003638  .007003   7.74787
{txt}major_~r*{c |}  {res} .0046284      .00705    0.66   0.511  -.009184  .018441   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean, no_dyad=1)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .32946136
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res} -.028726      .00828   -3.47   0.001  -.044963 -.012489   7.62051
 {txt}no_dyad {c |}  {res} .1810582      .15744    1.15   0.250   -.12751  .489626         1
{txt}pre_ac~d*{c |}  {res}-.2871594       .1125   -2.55   0.011  -.507662 -.066656    .74359
{txt}terri_~o*{c |}  {res}-.0608308      .14003   -0.43   0.664  -.335292  .213631   .205128
 {txt}polity2 {c |}  {res}-.0035256      .01461   -0.24   0.809  -.032155  .025104   .297436
{txt}mediat~1*{c |}  {res} .1877866      .09259    2.03   0.043   .006306  .369267   .661538
{txt}ethnic~a {c |}  {res}-.2248907       .2585   -0.87   0.384  -.731544  .281763   .617075
  {txt}ln_pop {c |}  {res} .0301153      .04594    0.66   0.512  -.059926  .120156   16.1888
{txt}inf~1000 {c |}  {res} .0029387      .00154    1.90   0.057  -.000088  .005965   80.8523
{txt}ln_dur~n {c |}  {res} .0674018      .04948    1.36   0.173  -.029577   .16438   7.74787
{txt}major_~r*{c |}  {res} .1832471      .07831    2.34   0.019   .029754   .33674   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, no_dyad=3)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .71677737
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0263972      .01515   -1.74   0.081  -.056086  .003291   7.62051
 {txt}no_dyad {c |}  {res} .1663804      .05324    3.12   0.002   .062025  .270736         3
{txt}pre_ac~d*{c |}  {res}-.2147848      .18187   -1.18   0.238  -.571245  .141675    .74359
{txt}terri_~o*{c |}  {res} -.059657       .1568   -0.38   0.704  -.366977  .247663   .205128
 {txt}polity2 {c |}  {res}-.0032398       .0135   -0.24   0.810  -.029697  .023217   .297436
{txt}mediat~1*{c |}  {res} .1930726      .14307    1.35   0.177  -.087345   .47349   .661538
{txt}ethnic~a {c |}  {res}-.2066596      .26143   -0.79   0.429  -.719054  .305735   .617075
  {txt}ln_pop {c |}  {res} .0276739      .03673    0.75   0.451  -.044311  .099659   16.1888
{txt}inf~1000 {c |}  {res} .0027005      .00173    1.56   0.118  -.000682  .006083   80.8523
{txt}ln_dur~n {c |}  {res} .0619377      .05796    1.07   0.285  -.051663  .175538   7.74787
{txt}major_~r*{c |}  {res}  .174223      .10187    1.71   0.087  -.025444   .37389   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean, pre_accord=0)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .58090826
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0316565      .01032   -3.07   0.002   -.05189 -.011423   7.62051
 {txt}no_dyad {c |}  {res} .1995292      .18247    1.09   0.274  -.158102  .557161   1.14872
{txt}pre_ac~d*{c |}  {res} -.292748      .11192   -2.62   0.009  -.512104 -.073392         0
{txt}terri_~o*{c |}  {res} -.069863      .16635   -0.42   0.674  -.395897  .256171   .205128
 {txt}polity2 {c |}  {res}-.0038853      .01606   -0.24   0.809  -.035371  .027601   .297436
{txt}mediat~1*{c |}  {res} .2208963      .10553    2.09   0.036    .01406  .427733   .661538
{txt}ethnic~a {c |}  {res}-.2478335      .28557   -0.87   0.385  -.807537   .31187   .617075
  {txt}ln_pop {c |}  {res} .0331875      .05089    0.65   0.514   -.06656  .132936   16.1888
{txt}inf~1000 {c |}  {res} .0032385      .00174    1.86   0.062  -.000168  .006645   80.8523
{txt}ln_dur~n {c |}  {res} .0742779      .05385    1.38   0.168  -.031273  .179829   7.74787
{txt}major_~r*{c |}  {res} .2051949      .08707    2.36   0.018   .034541  .375849   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, pre_accord=1)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .28816022
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0266724      .00818   -3.26   0.001  -.042699 -.010646   7.62051
 {txt}no_dyad {c |}  {res} .1681149       .1548    1.09   0.277   -.13528   .47151   1.14872
{txt}pre_ac~d*{c |}  {res} -.292748      .11192   -2.62   0.009  -.512104 -.073392         1
{txt}terri_~o*{c |}  {res}-.0561179       .1272   -0.44   0.659  -.305417  .193181   .205128
 {txt}polity2 {c |}  {res}-.0032736      .01361   -0.24   0.810  -.029949  .023402   .297436
{txt}mediat~1*{c |}  {res} .1729233      .08557    2.02   0.043   .005201  .340646   .661538
{txt}ethnic~a {c |}  {res} -.208814      .24449   -0.85   0.393  -.687999  .270371   .617075
  {txt}ln_pop {c |}  {res} .0279624      .04394    0.64   0.525  -.058165   .11409   16.1888
{txt}inf~1000 {c |}  {res} .0027286      .00148    1.85   0.065  -.000167  .005624   80.8523
{txt}ln_dur~n {c |}  {res} .0625834      .04496    1.39   0.164  -.025529  .150696   7.74787
{txt}major_~r*{c |}  {res} .1700579      .07674    2.22   0.027   .019643  .320473   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean, terri_inco=0)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .37040707
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0303239      .00922   -3.29   0.001  -.048402 -.012246   7.62051
 {txt}no_dyad {c |}  {res}   .19113      .17166    1.11   0.266  -.145327  .527587   1.14872
{txt}pre_ac~d*{c |}  {res}-.2947649      .11306   -2.61   0.009   -.51635  -.07318    .74359
{txt}terri_~o*{c |}  {res}-.0634779      .14614   -0.43   0.664  -.349907  .222951         0
 {txt}polity2 {c |}  {res}-.0037217      .01537   -0.24   0.809  -.033847  .026404   .297436
{txt}mediat~1*{c |}  {res} .1999972      .10106    1.98   0.048   .001925  .398069   .661538
{txt}ethnic~a {c |}  {res}-.2374009      .27288   -0.87   0.384  -.772242  .297441   .617075
  {txt}ln_pop {c |}  {res} .0317905      .04888    0.65   0.515  -.064015  .127596   16.1888
{txt}inf~1000 {c |}  {res} .0031022      .00162    1.91   0.056  -.000076   .00628   80.8523
{txt}ln_dur~n {c |}  {res} .0711512      .05171    1.38   0.169  -.030197  .172499   7.74787
{txt}major_~r*{c |}  {res} .1936538        .085    2.28   0.023   .027055  .360252   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, terri_inco=1)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .30692913
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0276606      .01037   -2.67   0.008  -.047981  -.00734   7.62051
 {txt}no_dyad {c |}  {res} .1743434      .17008    1.03   0.305  -.159013    .5077   1.14872
{txt}pre_ac~d*{c |}  {res}-.2810199      .10783   -2.61   0.009   -.49236  -.06968    .74359
{txt}terri_~o*{c |}  {res}-.0634779      .14614   -0.43   0.664  -.349907  .222951         1
 {txt}polity2 {c |}  {res}-.0033949      .01426   -0.24   0.812  -.031347  .024558   .297436
{txt}mediat~1*{c |}  {res} .1799923      .07652    2.35   0.019   .030014  .329971   .661538
{txt}ethnic~a {c |}  {res}-.2165504      .25047   -0.86   0.387   -.70746  .274359   .617075
  {txt}ln_pop {c |}  {res} .0289984      .04502    0.64   0.520  -.059246  .117242   16.1888
{txt}inf~1000 {c |}  {res} .0028297      .00186    1.52   0.129  -.000823  .006482   80.8523
{txt}ln_dur~n {c |}  {res} .0649021      .05317    1.22   0.222  -.039306   .16911   7.74787
{txt}major_~r*{c |}  {res} .1763878      .08087    2.18   0.029    .01788  .334896   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean, mediation_1=0)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .23364998
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res} -.023283      .00667   -3.49   0.000  -.036352 -.010214   7.62051
 {txt}no_dyad {c |}  {res} .1467516      .13795    1.06   0.287  -.123622  .417125   1.14872
{txt}pre_ac~d*{c |}  {res}-.2503582      .10438   -2.40   0.016  -.454931 -.045785    .74359
{txt}terri_~o*{c |}  {res}-.0485764      .10557   -0.46   0.645  -.255484  .158331   .205128
 {txt}polity2 {c |}  {res}-.0028576      .01194   -0.24   0.811  -.026262  .020547   .297436
{txt}mediat~1*{c |}  {res} .1962569      .09485    2.07   0.039   .010353  .382161         0
{txt}ethnic~a {c |}  {res}-.1822788      .20703   -0.88   0.379  -.588059  .223502   .617075
  {txt}ln_pop {c |}  {res} .0244091      .03689    0.66   0.508  -.047886  .096704   16.1888
{txt}inf~1000 {c |}  {res} .0023819      .00137    1.74   0.082  -.000305  .005069   80.8523
{txt}ln_dur~n {c |}  {res} .0546306      .04198    1.30   0.193  -.027641  .136902   7.74787
{txt}major_~r*{c |}  {res}  .148478      .06793    2.19   0.029   .015333  .281623   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, mediation_1=1)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .42990685
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0318688      .01002   -3.18   0.001  -.051508  -.01223   7.62051
 {txt}no_dyad {c |}  {res} .2008677      .18111    1.11   0.267  -.154098  .555833   1.14872
{txt}pre_ac~d*{c |}  {res}-.2983312      .11096   -2.69   0.007    -.5158 -.080862    .74359
{txt}terri_~o*{c |}  {res}-.0685814      .16076   -0.43   0.670  -.383674  .246512   .205128
 {txt}polity2 {c |}  {res}-.0039113      .01616   -0.24   0.809  -.035575  .027753   .297436
{txt}mediat~1*{c |}  {res} .1962569      .09485    2.07   0.039   .010353  .382161         1
{txt}ethnic~a {c |}  {res}-.2494959      .28606   -0.87   0.383  -.810166  .311174   .617075
  {txt}ln_pop {c |}  {res} .0334102      .05128    0.65   0.515  -.067088  .133908   16.1888
{txt}inf~1000 {c |}  {res} .0032603      .00174    1.87   0.062  -.000158  .006678   80.8523
{txt}ln_dur~n {c |}  {res} .0747762      .05498    1.36   0.174  -.032989  .182541   7.74787
{txt}major_~r*{c |}  {res} .2040565      .08751    2.33   0.020   .032539  .375574   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean,  ln_duration = 2.64)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .10459788
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0121783      .01168   -1.04   0.297  -.035061  .010705   7.62051
 {txt}no_dyad {c |}  {res} .0767593      .09768    0.79   0.432  -.114696  .268215   1.14872
{txt}pre_ac~d*{c |}  {res}-.1473361      .11361   -1.30   0.195  -.370014  .075342    .74359
{txt}terri_~o*{c |}  {res}-.0249215      .06593   -0.38   0.705  -.154136  .104294   .205128
 {txt}polity2 {c |}  {res}-.0014947       .0063   -0.24   0.812   -.01384  .010851   .297436
{txt}mediat~1*{c |}  {res} .0766751      .08737    0.88   0.380  -.094573  .247923   .661538
{txt}ethnic~a {c |}  {res} -.095342      .16689   -0.57   0.568  -.422431  .231747   .617075
  {txt}ln_pop {c |}  {res} .0127673      .02582    0.49   0.621  -.037843  .063378   16.1888
{txt}inf~1000 {c |}  {res} .0012459      .00114    1.09   0.275  -.000993  .003484   80.8523
{txt}ln_dur~n {c |}  {res} .0285748      .00716    3.99   0.000   .014534  .042615      2.64
{txt}major_~r*{c |}  {res} .0780176      .09109    0.86   0.392  -.100518  .256554   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean,  ln_duration = 9.81)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res}  .5101039
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0324944      .01026   -3.17   0.002  -.052607 -.012381   7.62051
 {txt}no_dyad {c |}  {res} .2048106      .18484    1.11   0.268  -.157473  .567094   1.14872
{txt}pre_ac~d*{c |}  {res} -.290616      .10045   -2.89   0.004  -.487492  -.09374    .74359
{txt}terri_~o*{c |}  {res}-.0708589       .1671   -0.42   0.672  -.398364  .256646   .205128
 {txt}polity2 {c |}  {res}-.0039881      .01646   -0.24   0.809  -.036247   .02827   .297436
{txt}mediat~1*{c |}  {res} .2220138      .10514    2.11   0.035   .015934  .428093   .661538
{txt}ethnic~a {c |}  {res}-.2543934      .29033   -0.88   0.381  -.823422  .314635   .617075
  {txt}ln_pop {c |}  {res}  .034066      .05176    0.66   0.510  -.067391  .135523   16.1888
{txt}inf~1000 {c |}  {res} .0033242      .00178    1.87   0.061  -.000156  .006804   80.8523
{txt}ln_dur~n {c |}  {res}  .076244      .05582    1.37   0.172  -.033168  .185655      9.81
{txt}major_~r*{c |}  {res} .2092097      .08814    2.37   0.018   .036454  .381965   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean,  major_war = 0)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .25830327
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0249116      .00742   -3.36   0.001  -.039463  -.01036   7.62051
 {txt}no_dyad {c |}  {res} .1570168      .13874    1.13   0.258  -.114901  .428935   1.14872
{txt}pre_ac~d*{c |}  {res}-.2626106      .10811   -2.43   0.015  -.474496 -.050726    .74359
{txt}terri_~o*{c |}  {res}-.0521715      .11754   -0.44   0.657  -.282536  .178193   .205128
 {txt}polity2 {c |}  {res}-.0030575      .01276   -0.24   0.811  -.028066  .021951   .297436
{txt}mediat~1*{c |}  {res}  .160607      .07942    2.02   0.043    .00494  .316274   .661538
{txt}ethnic~a {c |}  {res}-.1950292      .23075   -0.85   0.398  -.647281  .257223   .617075
  {txt}ln_pop {c |}  {res} .0261165      .03952    0.66   0.509  -.051344  .103577   16.1888
{txt}inf~1000 {c |}  {res} .0025485       .0014    1.82   0.068  -.000192  .005289   80.8523
{txt}ln_dur~n {c |}  {res}  .058452      .04515    1.29   0.195  -.030035  .146939   7.74787
{txt}major_~r*{c |}  {res} .1905199      .08257    2.31   0.021    .02868  .352359         0
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean,  major_war = 1)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .44882315
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0321671      .01006   -3.20   0.001  -.051881 -.012453   7.62051
 {txt}no_dyad {c |}  {res} .2027478      .18432    1.10   0.271  -.158509  .564005   1.14872
{txt}pre_ac~d*{c |}  {res}-.2977475       .1103   -2.70   0.007  -.513931 -.081564    .74359
{txt}terri_~o*{c |}  {res}-.0694375      .16253   -0.43   0.669  -.387983  .249108   .205128
 {txt}polity2 {c |}  {res} -.003948       .0163   -0.24   0.809  -.035891  .027995   .297436
{txt}mediat~1*{c |}  {res} .2161855      .10395    2.08   0.038   .012444  .419927   .661538
{txt}ethnic~a {c |}  {res}-.2518312       .2863   -0.88   0.379  -.812978  .309315   .617075
  {txt}ln_pop {c |}  {res} .0337229      .05168    0.65   0.514  -.067569  .135014   16.1888
{txt}inf~1000 {c |}  {res} .0032908      .00177    1.86   0.063  -.000173  .006754   80.8523
{txt}ln_dur~n {c |}  {res} .0754761      .05533    1.36   0.173  -.032968   .18392   7.74787
{txt}major_~r*{c |}  {res} .1905199      .08257    2.31   0.021    .02868  .352359         1
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean, polity=-9)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .39165555
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0309813      .01004   -3.09   0.002  -.050661 -.011302   7.62051
 {txt}no_dyad {c |}  {res} .1952737      .17834    1.09   0.274  -.154269  .544816   1.14872
{txt}pre_ac~d*{c |}  {res} -.297014      .11006   -2.70   0.007  -.512735 -.081293    .74359
{txt}terri_~o*{c |}  {res}-.0662599      .15116   -0.44   0.661  -.362523  .230004   .205128
 {txt}polity2 {c |}  {res}-.0038024      .01624   -0.23   0.815  -.035623  .028018        -9
{txt}mediat~1*{c |}  {res} .2053279      .09981    2.06   0.040   .009712  .400944   .661538
{txt}ethnic~a {c |}  {res}-.2425477       .2954   -0.82   0.412  -.821519  .336423   .617075
  {txt}ln_pop {c |}  {res} .0324797      .05049    0.64   0.520  -.066471   .13143   16.1888
{txt}inf~1000 {c |}  {res} .0031695      .00163    1.94   0.053  -.000035  .006374   80.8523
{txt}ln_dur~n {c |}  {res} .0726937      .05508    1.32   0.187  -.035259  .180647   7.74787
{txt}major_~r*{c |}  {res} .1980104      .08335    2.38   0.018   .034642  .361379   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, polity=10)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .32222183
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0283981        .011   -2.58   0.010  -.049958 -.006838   7.62051
 {txt}no_dyad {c |}  {res} .1789915      .16756    1.07   0.285  -.149426  .507409   1.14872
{txt}pre_ac~d*{c |}  {res}-.2853436      .12352   -2.31   0.021  -.527434 -.043253    .74359
{txt}terri_~o*{c |}  {res}-.0600679      .14196   -0.42   0.672   -.33831  .218174   .205128
 {txt}polity2 {c |}  {res}-.0034854      .01365   -0.26   0.798  -.030234  .023263        10
{txt}mediat~1*{c |}  {res}  .185365      .10711    1.73   0.084  -.024574  .395304   .661538
{txt}ethnic~a {c |}  {res}-.2223237      .23396   -0.95   0.342  -.680879  .236232   .617075
  {txt}ln_pop {c |}  {res} .0297715      .04561    0.65   0.514  -.059628  .119171   16.1888
{txt}inf~1000 {c |}  {res} .0029052      .00186    1.56   0.118  -.000734  .006545   80.8523
{txt}ln_dur~n {c |}  {res} .0666324      .05015    1.33   0.184  -.031666  .164931   7.74787
{txt}major_~r*{c |}  {res} .1811311       .0969    1.87   0.062  -.008784  .371047   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean, ethnic_fra = 0.25)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .44644187
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0321347      .01005   -3.20   0.001  -.051836 -.012434   7.62051
 {txt}no_dyad {c |}  {res} .2025433      .18351    1.10   0.270  -.157127  .562213   1.14872
{txt}pre_ac~d*{c |}  {res}-.2978645      .11186   -2.66   0.008  -.517101 -.078628    .74359
{txt}terri_~o*{c |}  {res}-.0693404      .16232   -0.43   0.669  -.387482  .248801   .205128
 {txt}polity2 {c |}  {res} -.003944      .01639   -0.24   0.810  -.036068   .02818   .297436
{txt}mediat~1*{c |}  {res} .2158361      .10713    2.01   0.044   .005858  .425815   .661538
{txt}ethnic~a {c |}  {res}-.2515773      .29858   -0.84   0.399  -.836774   .33362       .25
  {txt}ln_pop {c |}  {res} .0336889      .05171    0.65   0.515  -.067669  .135047   16.1888
{txt}inf~1000 {c |}  {res} .0032874      .00178    1.85   0.064  -.000197  .006772   80.8523
{txt}ln_dur~n {c |}  {res}    .0754      .05444    1.39   0.166  -.031292  .182091   7.74787
{txt}major_~r*{c |}  {res} .2059543      .08543    2.41   0.016   .038524  .373385   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, ethnic_fra = 0.93) 

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .28755463
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res} -.026639      .00891   -2.99   0.003  -.044096 -.009182   7.62051
 {txt}no_dyad {c |}  {res} .1679043      .15386    1.09   0.275  -.133657  .469466   1.14872
{txt}pre_ac~d*{c |}  {res} -.274559      .11968   -2.29   0.022  -.509118     -.04    .74359
{txt}terri_~o*{c |}  {res}-.0560423      .12731   -0.44   0.660  -.305568  .193483   .205128
 {txt}polity2 {c |}  {res}-.0032695      .01331   -0.25   0.806  -.029352  .022813   .297436
{txt}mediat~1*{c |}  {res} .1726865      .08407    2.05   0.040    .00792  .337453   .661538
{txt}ethnic~a {c |}  {res}-.2085524      .20848   -1.00   0.317  -.617162  .200057       .93
  {txt}ln_pop {c |}  {res} .0279274      .04259    0.66   0.512  -.055544  .111399   16.1888
{txt}inf~1000 {c |}  {res} .0027252      .00151    1.81   0.070  -.000227  .005677   80.8523
{txt}ln_dur~n {c |}  {res}  .062505      .05041    1.24   0.215  -.036306  .161316   7.74787
{txt}major_~r*{c |}  {res} .1698444      .08503    2.00   0.046   .003191  .336498   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean, ln_pop = 13.21)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .26996278
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0256268       .0079   -3.24   0.001  -.041113 -.010141   7.62051
 {txt}no_dyad {c |}  {res} .1615246      .13099    1.23   0.218  -.095211   .41826   1.14872
{txt}pre_ac~d*{c |}  {res}-.2676995      .13559   -1.97   0.048  -.533458 -.001941    .74359
{txt}terri_~o*{c |}  {res} -.053766      .12246   -0.44   0.661  -.293784  .186252   .205128
 {txt}polity2 {c |}  {res}-.0031452      .01297   -0.24   0.808   -.02856   .02227   .297436
{txt}mediat~1*{c |}  {res} .1655726      .07908    2.09   0.036   .010586  .320559   .661538
{txt}ethnic~a {c |}  {res}-.2006283      .22616   -0.89   0.375  -.643886  .242629   .617075
  {txt}ln_pop {c |}  {res} .0268663      .03406    0.79   0.430   -.03989  .093623     13.21
{txt}inf~1000 {c |}  {res} .0026217      .00148    1.77   0.077  -.000282  .005526   80.8523
{txt}ln_dur~n {c |}  {res} .0601301      .05083    1.18   0.237  -.039501  .159761   7.74787
{txt}major_~r*{c |}  {res} .1633841       .0655    2.49   0.013   .035014  .291754   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, ln_pop = 20.63)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .50416993
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0325054      .01018   -3.19   0.001  -.052465 -.012546   7.62051
 {txt}no_dyad {c |}  {res}   .20488      .18431    1.11   0.266   -.15636   .56612   1.14872
{txt}pre_ac~d*{c |}  {res} -.291643      .12838   -2.27   0.023  -.543258 -.040028    .74359
{txt}terri_~o*{c |}  {res}-.0708127      .16711   -0.42   0.672  -.398336   .25671   .205128
 {txt}polity2 {c |}  {res}-.0039895      .01646   -0.24   0.809   -.03626  .028281   .297436
{txt}mediat~1*{c |}  {res} .2217194      .11041    2.01   0.045   .005329   .43811   .661538
{txt}ethnic~a {c |}  {res}-.2544797      .28918   -0.88   0.379  -.821261  .312301   .617075
  {txt}ln_pop {c |}  {res} .0340775      .05146    0.66   0.508  -.066785   .13494     20.63
{txt}inf~1000 {c |}  {res} .0033254      .00178    1.86   0.062   -.00017  .006821   80.8523
{txt}ln_dur~n {c |}  {res} .0762698      .05669    1.35   0.179  -.034848  .187388   7.74787
{txt}major_~r*{c |}  {res} .2091793      .08917    2.35   0.019   .034411  .383947   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. mfx compute, at (mean,  infantmort_rate1000 = 4.30)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .16699608
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0180884      .00788   -2.30   0.022  -.033534 -.002643   7.62051
 {txt}no_dyad {c |}  {res} .1140101       .0968    1.18   0.239  -.075705  .303725   1.14872
{txt}pre_ac~d*{c |}  {res}-.2060621      .10506   -1.96   0.050  -.411971 -.000153    .74359
{txt}terri_~o*{c |}  {res}-.0373598      .09077   -0.41   0.681  -.215273  .140553   .205128
 {txt}polity2 {c |}  {res}  -.00222       .0095   -0.23   0.815  -.020838  .016398   .297436
{txt}mediat~1*{c |}  {res} .1148704      .07687    1.49   0.135  -.035794  .265535   .661538
{txt}ethnic~a {c |}  {res}-.1416109      .17101   -0.83   0.408  -.476779  .193557   .617075
  {txt}ln_pop {c |}  {res} .0189632      .02972    0.64   0.523  -.039288  .077215   16.1888
{txt}inf~1000 {c |}  {res} .0018505       .0004    4.64   0.000   .001068  .002633       4.3
{txt}ln_dur~n {c |}  {res}  .042442      .02978    1.43   0.154  -.015926  .100811   7.74787
{txt}major_~r*{c |}  {res} .1155405      .06891    1.68   0.094   -.01952  .250601   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean,  infantmort_rate1000 = 161.30)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .61808153
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
pam_uc~l {c |}  {res}-.0306946      .01036   -2.96   0.003  -.050994 -.010396   7.62051
 {txt}no_dyad {c |}  {res} .1934667      .16638    1.16   0.245  -.132632  .519565   1.14872
{txt}pre_ac~d*{c |}  {res}-.2602935      .10471   -2.49   0.013  -.465516 -.055071    .74359
{txt}terri_~o*{c |}  {res}-.0681749      .16761   -0.41   0.684  -.396691  .260341   .205128
 {txt}polity2 {c |}  {res}-.0037672      .01568   -0.24   0.810  -.034495  .026961   .297436
{txt}mediat~1*{c |}  {res} .2167666      .10848    2.00   0.046   .004144  .429389   .661538
{txt}ethnic~a {c |}  {res}-.2403033      .27266   -0.88   0.378  -.774707  .294101   .617075
  {txt}ln_pop {c |}  {res} .0321792      .04804    0.67   0.503  -.061968  .126327   16.1888
{txt}inf~1000 {c |}  {res} .0031401      .00128    2.46   0.014    .00064   .00564     161.3
{txt}ln_dur~n {c |}  {res}  .072021      .05138    1.40   0.161  -.028686  .172728   7.74787
{txt}major_~r*{c |}  {res} .1998106      .08766    2.28   0.023   .028006  .371615   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. //Cumulative Marginal Effects of Peace Agreement 
. logit   dyvi05 total_p no_dyad pre_accord  terri_inco polity mediation_1 ethnic_fra  ln_pop infantmort_rate1000  ln_duration major_war, cl(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-131.23728}  
Iteration 1:{space 3}log pseudolikelihood = {res:-107.25356}  
Iteration 2:{space 3}log pseudolikelihood = {res:-106.82265}  
Iteration 3:{space 3}log pseudolikelihood = {res:-106.82173}  
Iteration 4:{space 3}log pseudolikelihood = {res:-106.82173}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}       195
{txt}{col 51}Wald chi2({res}11{txt}){col 67}= {res}     37.97
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0001
{txt}Log pseudolikelihood = {res}-106.82173{txt}{col 51}Pseudo R2{col 67}= {res}    0.1860

{txt}{ralign 85:(Std. Err. adjusted for {res:45} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             dyvi05{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}total_prov {c |}{col 21}{res}{space 2}-.2439496{col 33}{space 2} .0595916{col 44}{space 1}   -4.09{col 53}{space 3}0.000{col 61}{space 4} -.360747{col 74}{space 3}-.1271522
{txt}{space 12}no_dyad {c |}{col 21}{res}{space 2} .8001231{col 33}{space 2} .6872247{col 44}{space 1}    1.16{col 53}{space 3}0.244{col 61}{space 4}-.5468125{col 74}{space 3} 2.147059
{txt}{space 9}pre_accord {c |}{col 21}{res}{space 2} -1.19011{col 33}{space 2} .4826995{col 44}{space 1}   -2.47{col 53}{space 3}0.014{col 61}{space 4}-2.136184{col 74}{space 3}-.2440366
{txt}{space 9}terri_inco {c |}{col 21}{res}{space 2}-.1528061{col 33}{space 2} .6612423{col 44}{space 1}   -0.23{col 53}{space 3}0.817{col 61}{space 4}-1.448817{col 74}{space 3} 1.143205
{txt}{space 12}polity2 {c |}{col 21}{res}{space 2}-.0185839{col 33}{space 2}  .068633{col 44}{space 1}   -0.27{col 53}{space 3}0.787{col 61}{space 4}-.1531021{col 74}{space 3} .1159343
{txt}{space 8}mediation_1 {c |}{col 21}{res}{space 2}  .919822{col 33}{space 2}  .482762{col 44}{space 1}    1.91{col 53}{space 3}0.057{col 61}{space 4} -.026374{col 74}{space 3} 1.866018
{txt}{space 9}ethnic_fra {c |}{col 21}{res}{space 2} -.840554{col 33}{space 2} 1.141071{col 44}{space 1}   -0.74{col 53}{space 3}0.461{col 61}{space 4}-3.077013{col 74}{space 3} 1.395905
{txt}{space 13}ln_pop {c |}{col 21}{res}{space 2} .1467166{col 33}{space 2} .1961988{col 44}{space 1}    0.75{col 53}{space 3}0.455{col 61}{space 4} -.237826{col 74}{space 3} .5312593
{txt}infantmort_rate1000 {c |}{col 21}{res}{space 2} .0166272{col 33}{space 2} .0068692{col 44}{space 1}    2.42{col 53}{space 3}0.015{col 61}{space 4} .0031638{col 74}{space 3} .0300906
{txt}{space 8}ln_duration {c |}{col 21}{res}{space 2} .2661566{col 33}{space 2} .2272801{col 44}{space 1}    1.17{col 53}{space 3}0.242{col 61}{space 4}-.1793043{col 74}{space 3} .7116174
{txt}{space 10}major_war {c |}{col 21}{res}{space 2} 1.041485{col 33}{space 2} .3708285{col 44}{space 1}    2.81{col 53}{space 3}0.005{col 61}{space 4} .3146746{col 74}{space 3} 1.768296
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-5.599101{col 33}{space 2} 3.829106{col 44}{space 1}   -1.46{col 53}{space 3}0.144{col 61}{space 4}-13.10401{col 74}{space 3}  1.90581
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. mfx compute, at (mean, total_p=0)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res}  .6987076
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0513551      .00659   -7.79   0.000  -.064281 -.038429         0
 {txt}no_dyad {c |}  {res} .1684381       .1454    1.16   0.247  -.116536  .453412   1.14872
{txt}pre_ac~d*{c |}  {res}-.2180333       .0852   -2.56   0.010  -.385028 -.051038    .74359
{txt}terri_~o*{c |}  {res}-.0327261      .14335   -0.23   0.819  -.313691  .248239   .205128
 {txt}polity2 {c |}  {res}-.0039122      .01446   -0.27   0.787  -.032247  .024423   .297436
{txt}mediat~1*{c |}  {res} .2020604      .09663    2.09   0.037   .012679  .391442   .661538
{txt}ethnic~a {c |}  {res}-.1769495      .23947   -0.74   0.460  -.646311  .292412   .617075
  {txt}ln_pop {c |}  {res} .0308861      .03955    0.78   0.435  -.046623  .108395   16.1888
{txt}inf~1000 {c |}  {res} .0035003      .00144    2.43   0.015    .00068  .006321   80.8523
{txt}ln_dur~n {c |}  {res}   .05603      .04838    1.16   0.247  -.038799  .150859   7.74787
{txt}major_~r*{c |}  {res} .2206945      .07619    2.90   0.004   .071372  .370017   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, total_p=3)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .52729841
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0608056      .01431   -4.25   0.000  -.088859 -.032753         3
 {txt}no_dyad {c |}  {res} .1994345      .17107    1.17   0.244  -.135857  .534727   1.14872
{txt}pre_ac~d*{c |}  {res}-.2787354      .10344   -2.69   0.007  -.481473 -.075998    .74359
{txt}terri_~o*{c |}  {res}-.0381436      .16513   -0.23   0.817  -.361791  .285503   .205128
 {txt}polity2 {c |}  {res}-.0046321       .0171   -0.27   0.786   -.03815  .028885   .297436
{txt}mediat~1*{c |}  {res} .2259034      .11469    1.97   0.049   .001123  .450684   .661538
{txt}ethnic~a {c |}  {res}-.2095121      .28397   -0.74   0.461  -.766082  .347058   .617075
  {txt}ln_pop {c |}  {res} .0365698      .04868    0.75   0.453  -.058842  .131981   16.1888
{txt}inf~1000 {c |}  {res} .0041444       .0017    2.44   0.015   .000809   .00748   80.8523
{txt}ln_dur~n {c |}  {res} .0663408      .05667    1.17   0.242  -.044727  .177409   7.74787
{txt}major_~r*{c |}  {res} .2544387      .08634    2.95   0.003    .08522  .423657   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, total_p=6)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .34920252
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}  -.05544      .01367   -4.06   0.000  -.082227 -.028654         6
 {txt}no_dyad {c |}  {res} .1818361      .15762    1.15   0.249  -.127091  .490763   1.14872
{txt}pre_ac~d*{c |}  {res} -.281837      .11138   -2.53   0.011  -.500137 -.063537    .74359
{txt}terri_~o*{c |}  {res}-.0342308      .14547   -0.24   0.814  -.319346  .250884   .205128
 {txt}polity2 {c |}  {res}-.0042234      .01566   -0.27   0.787  -.034913  .026466   .297436
{txt}mediat~1*{c |}  {res} .1968177      .09838    2.00   0.045      .004  .389635   .661538
{txt}ethnic~a {c |}  {res}-.1910244      .26228   -0.73   0.466  -.705081  .323032   .617075
  {txt}ln_pop {c |}  {res} .0333428      .04525    0.74   0.461  -.055353  .122038   16.1888
{txt}inf~1000 {c |}  {res} .0037787       .0016    2.36   0.018   .000644  .006913   80.8523
{txt}ln_dur~n {c |}  {res} .0604868      .05145    1.18   0.240  -.040348  .161322   7.74787
{txt}major_~r*{c |}  {res} .2293987      .08084    2.84   0.005   .070953  .387844   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, total_p=9)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .20515273
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0397797       .0067   -5.94   0.000  -.052916 -.026643         9
 {txt}no_dyad {c |}  {res} .1304721      .11529    1.13   0.258  -.095496   .35644   1.14872
{txt}pre_ac~d*{c |}  {res}-.2249262       .0971   -2.32   0.021  -.415235 -.034618    .74359
{txt}terri_~o*{c |}  {res}-.0242571      .10094   -0.24   0.810  -.222092  .173578   .205128
 {txt}polity2 {c |}  {res}-.0030304      .01132   -0.27   0.789  -.025222  .019161   .297436
{txt}mediat~1*{c |}  {res} .1374036      .06376    2.16   0.031   .012443  .262364   .661538
{txt}ethnic~a {c |}  {res} -.137065       .1917   -0.72   0.475  -.512784  .238654   .617075
  {txt}ln_pop {c |}  {res} .0239244      .03239    0.74   0.460  -.039561   .08741   16.1888
{txt}inf~1000 {c |}  {res} .0027113      .00119    2.28   0.022   .000385  .005038   80.8523
{txt}ln_dur~n {c |}  {res} .0434008      .03688    1.18   0.239  -.028882  .115684   7.74787
{txt}major_~r*{c |}  {res} .1650609      .06186    2.67   0.008   .043809  .286313   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, total_p=12)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .11044103
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0239665      .00349   -6.86   0.000  -.030812 -.017121        12
 {txt}no_dyad {c |}  {res} .0786071      .07304    1.08   0.282  -.064555  .221769   1.14872
{txt}pre_ac~d*{c |}  {res}-.1474149      .07463   -1.98   0.048   -.29368  -.00115    .74359
{txt}terri_~o*{c |}  {res}-.0144976      .05922   -0.24   0.807   -.13057  .101575   .205128
 {txt}polity2 {c |}  {res}-.0018258      .00689   -0.26   0.791  -.015338  .011687   .297436
{txt}mediat~1*{c |}  {res} .0816472      .03739    2.18   0.029   .008373  .154922   .661538
{txt}ethnic~a {c |}  {res}-.0825792      .11909   -0.69   0.488  -.315991  .150832   .617075
  {txt}ln_pop {c |}  {res}  .014414      .01942    0.74   0.458  -.023643  .052471   16.1888
{txt}inf~1000 {c |}  {res} .0016335      .00082    1.99   0.046   .000028  .003239   80.8523
{txt}ln_dur~n {c |}  {res} .0261482      .02298    1.14   0.255  -.018891  .071187   7.74787
{txt}major_~r*{c |}  {res} .1002096      .04516    2.22   0.027   .011688  .188731   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, total_p=15)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .05635427
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0129729      .00376   -3.45   0.001  -.020336  -.00561        15
 {txt}no_dyad {c |}  {res} .0425493      .04305    0.99   0.323  -.041834  .126933   1.14872
{txt}pre_ac~d*{c |}  {res}-.0842452      .05311   -1.59   0.113  -.188332  .019842    .74359
{txt}terri_~o*{c |}  {res} -.007812      .03148   -0.25   0.804  -.069511  .053888   .205128
 {txt}polity2 {c |}  {res}-.0009883      .00378   -0.26   0.794  -.008398  .006421   .297436
{txt}mediat~1*{c |}  {res} .0439103      .02286    1.92   0.055   -.00089  .088711   .661538
{txt}ethnic~a {c |}  {res}-.0446994      .06745   -0.66   0.507  -.176891  .087493   .617075
  {txt}ln_pop {c |}  {res} .0078022       .0106    0.74   0.462  -.012974  .028578   16.1888
{txt}inf~1000 {c |}  {res} .0008842      .00055    1.61   0.108  -.000195  .001963   80.8523
{txt}ln_dur~n {c |}  {res} .0141538      .01345    1.05   0.293  -.012204  .040512   7.74787
{txt}major_~r*{c |}  {res} .0545949      .03183    1.72   0.086  -.007795  .116984   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, total_p=18)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .02792416
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0066219      .00314   -2.11   0.035  -.012767 -.000477        18
 {txt}no_dyad {c |}  {res} .0217189      .02439    0.89   0.373  -.026078  .069515   1.14872
{txt}pre_ac~d*{c |}  {res}-.0443386      .03478   -1.27   0.202  -.112509  .023832    .74359
{txt}terri_~o*{c |}  {res}-.0039781      .01589   -0.25   0.802  -.035121  .027164   .205128
 {txt}polity2 {c |}  {res}-.0005044      .00196   -0.26   0.797  -.004343  .003334   .297436
{txt}mediat~1*{c |}  {res} .0223467      .01426    1.57   0.117  -.005596  .050289   .661538
{txt}ethnic~a {c |}  {res}-.0228163       .0364   -0.63   0.531  -.094166  .048533   .617075
  {txt}ln_pop {c |}  {res} .0039825      .00555    0.72   0.473  -.006904  .014869   16.1888
{txt}inf~1000 {c |}  {res} .0004513      .00035    1.29   0.197  -.000235  .001137   80.8523
{txt}ln_dur~n {c |}  {res} .0072247      .00762    0.95   0.343  -.007713  .022162   7.74787
{txt}major_~r*{c |}  {res} .0279799       .0208    1.34   0.179  -.012795  .068755   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. mfx compute, at (mean, total_p=21)

{txt}Marginal effects after logit
      y  = Pr(dyvi05) (predict)
         = {res} .01362957
{txt}{hline 9}{c TT}{hline 68}
variable {c |}{col 17}dy/dx{col 26}Std. Err.{col 40}z{col 45}P>|z|{col 52}[    95% C.I.   ]{col 75}X
{hline 9}{c +}{hline 68}
total_~v {c |}  {res}-.0032796      .00217   -1.51   0.130  -.007525  .000965        21
 {txt}no_dyad {c |}  {res} .0107567      .01346    0.80   0.424  -.015624  .037138   1.14872
{txt}pre_ac~d*{c |}  {res}-.0223132      .02119   -1.05   0.292  -.063852  .019226    .74359
{txt}terri_~o*{c |}  {res}-.0019679      .00782   -0.25   0.801  -.017297  .013361   .205128
 {txt}polity2 {c |}  {res}-.0002498      .00099   -0.25   0.800  -.002182  .001682   .297436
{txt}mediat~1*{c |}  {res} .0110522      .00865    1.28   0.202   -.00591  .028014   .661538
{txt}ethnic~a {c |}  {res}-.0113002      .01917   -0.59   0.556  -.048882  .026282   .617075
  {txt}ln_pop {c |}  {res} .0019724      .00287    0.69   0.491  -.003646  .007591   16.1888
{txt}inf~1000 {c |}  {res} .0002235      .00021    1.06   0.288  -.000188  .000635   80.8523
{txt}ln_dur~n {c |}  {res} .0035782      .00422    0.85   0.397    -.0047  .011856   7.74787
{txt}major_~r*{c |}  {res}  .013888      .01269    1.09   0.274  -.010976  .038752   .548718
{txt}{hline 9}{c BT}{hline 68}
(*) dy/dx is for discrete change of dummy variable from 0 to 1

{com}. 
. 
. //for vif tests use replace logit with collin and drop cl(ccode)
. clear
{txt}
{com}. clear matrix
{txt}
{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\mjoshi2.DSS-OKIU3QJMAS9\Desktop\Accepted-Final Articles 2014\Negotiation Journal\Negotiaiton Journal\data and do files\log.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}15 Jan 2015, 17:00:50
{txt}{.-}
{smcl}
{txt}{sf}{ul off}